Course Information

Bi7490 Advanced non-parametric methods

Extent and Intensity

1/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Taught online.

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Timetable

Fri 12:00–13:50 D29/347-RCX2

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

The aim of the course is to provide students with knowledge regarding basic and advanced nonparametric methods for classification and regression and teach them how to apply these methods in different software tools (R-project, Matlab, Statistica).

Learning outcomes

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• 1. Introduction to Nonparametric Methods - Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB).
• 2. Decision tree I - tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees.
• 3. Decision tree II - another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS).
• 4. Random Forests I - extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing.
• 5. Random Forests II - measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction.
• 6. Accuracy of models I - matrix of confusion, definition of threshold dependent and independent indexes. Threshold dependent indexes: Normalized Mutual Information (MI),Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index.
• 7. Accuracy of models II - threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE).
• 8. Validation technique I - validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM).
• 9. Validation technique II - Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife.
• 10. Real examples of using nonparametric models: Predictive modeling of species occurrence, concentration of pollutants.

Literature

• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.iba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

Extent and Intensity

1/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Taught online.

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Timetable

Fri 12:00–13:50 D29/347-RCX2

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

The aim of the course is to provide students with knowledge regarding basic and advanced nonparametric methods for classification and regression and teach them how to apply these methods in different software tools (R-project, Matlab, Statistica).

Learning outcomes

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• 1. Introduction to Nonparametric Methods - Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB).
• 2. Decision tree I - tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees.
• 3. Decision tree II - another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS).
• 4. Random Forests I - extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing.
• 5. Random Forests II - measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction.
• 6. Accuracy of models I - matrix of confusion, definition of threshold dependent and independent indexes. Threshold dependent indexes: Normalized Mutual Information (MI),Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index.
• 7. Accuracy of models II - threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE).
• 8. Validation technique I - validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM).
• 9. Validation technique II - Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife.
• 10. Real examples of using nonparametric models: Predictive modeling of species occurrence, concentration of pollutants.

Literature

• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.iba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

The aim of the course is to provide students with knowledge regarding basic and advanced nonparametric methods for classification and regression and teach them how to apply these methods in different software tools (R-project, Matlab, Statistica).

Learning outcomes

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• 1. Introduction to Nonparametric Methods - Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB).
• 2. Decision tree I - tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees.
• 3. Decision tree II - another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS).
• 4. Random Forests I - extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing.
• 5. Random Forests II - measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction.
• 6. Accuracy of models I - matrix of confusion, definition of threshold dependent and independent indexes. Threshold dependent indexes: Normalized Mutual Information (MI),Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index.
• 7. Accuracy of models II - threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE).
• 8. Validation technique I - validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM).
• 9. Validation technique II - Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife.
• 10. Real examples of using nonparametric models: Predictive modeling of species occurrence, concentration of pollutants.

Literature

• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.iba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Timetable

Thu 13:00–15:50 D29/347-RCX2

Prerequisites (in Czech)

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Timetable

Thu 13:00–15:50 D29/347-RCX2

Prerequisites (in Czech)

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Timetable

Wed 13:00–15:50 D29/347-RCX2

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Timetable

Thu 16:00–19:50 F01B1/709

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• General Biology (programme PřF, N-BI, specialization Ekotoxikologie)
• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohenovo kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, crossvalidation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (seminar tutor)
prof. Ing. Jiří Holčík, CSc. (alternate examiner)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Timetable

Wed 17:00–19:50 F01B1/709

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Statistical Meth.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• General Biology (programme PřF, N-BI, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohenovo kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, crossvalidation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Predictive Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Klára Komprdová, Ph.D. (seminar tutor)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Timetable

Mon 13:00–16:50 F01B1/709

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Statistical Meth.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 11 fields of study the course is directly associated with, display

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- determine the space structure of the data
- use basic methods for spatial and predictive modeling
- acquisition of various software to create models(R-project, Matlab, Statistica)
- select an appropriate predictive method based on the distribution of data
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to predictive modelling: Principles of multivariate statistics, Comparison of parametric and nonparametric methods, Demonstration various software (STATISTIKA, R-project, MATLAB)
• Parametric regression methods (LM, GLM, GAM): Assumptions, Limitations, and Practical Considerations (selection of link function, multicolinearity, estimate parameters, residuals, deviance etc.)
• Nonparametric methods I: Decision tree: Classification and regression tree (various algorithm of building tree, accuracy, stability, crossvalidation etc.)
• Nonparametric methods II: Bagging, Boosting, Arcing, Random forest
• Spatial analysis: Interpolation and Extrapolation, Spatial autocorrelation, Pseudoreplication, using parametrical and nonparametric methods for spatial modelling
• Real examples of predicting modelling: Predictive modelling of species occurrence, concentration of pollutants; selection indicative species

Literature

• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Hengl T. (2007) A Practical Guide to Geostatistical Mapping of Environmental Variables
• Lemeshow, Stanley & Hosmer, David W., Jr.. Logistic regression, p. 1-11. In Encyclopaedia of Biostatistics, 1st ed. [Online.] Wiley, London.
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh, P., Nelder, J.A. (1989): Generalized Linear Models (2nd edition), Chapman & Hall
• Harrel F. E., Jr. (2001): Regression Modeling Strategies. With Applications to Linear Models, Logistic Regression and Survival Analysis. Springer, Springer Series in Statistics, New York
• Lažanský et. Kol.: Umělá inteligence I.- IV.

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Predictive Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Klára Komprdová, Ph.D. (seminar tutor)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Statistical Meth.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 11 fields of study the course is directly associated with, display

Course objectives

This course focuses on using of advanced parametric and non-parametric multivariate methods for spatial and predictive modelling (from basic regression continues to the latest non-parametric methods). Important subject is a comparison of advantages and disadvantages of individual methods on different data sets (from statistical and spatial distribution point of view). Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures.

Syllabus

• Introduction to predictive modelling: Principles of multivariate statistics, Comparison of parametric and nonparametric methods, Demonstration various software (STATISTIKA, R-project, MATLAB)
• Parametric regression methods (LM, GLM, GAM): Assumptions, Limitations, and Practical Considerations (selection of link function, multicolinearity, estimate parameters, residuals, deviance etc.)
• Nonparametric methods I: Decision tree: Classification and regression tree (various algorithm of building tree, accuracy, stability, crossvalidation etc.)
• Nonparametric methods II: Bagging, Boosting, Arcing, Random forest
• Spatial analysis: Interpolation and Extrapolation, Spatial autocorrelation, Pseudoreplication, using parametrical and nonparametric methods for spatial modelling
• Real examples of predicting modelling: Predictive modelling of species occurrence, concentration of pollutants; selection indicative species

Literature

• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Hengl T. (2007) A Practical Guide to Geostatistical Mapping of Environmental Variables
• Lemeshow, Stanley & Hosmer, David W., Jr.. Logistic regression, p. 1-11. In Encyclopaedia of Biostatistics, 1st ed. [Online.] Wiley, London.
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• McCullagh, P., Nelder, J.A. (1989): Generalized Linear Models (2nd edition), Chapman & Hall
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Harrel F. E., Jr. (2001): Regression Modeling Strategies. With Applications to Linear Models, Logistic Regression and Survival Analysis. Springer, Springer Series in Statistics, New York

Assessment methods

lectures and practice on PC; written tests

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Eva Gelnarová (seminar tutor)
Mgr. Klára Komprdová, Ph.D. (seminar tutor)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Timetable

Tue 8:00–11:50 Kontaktujte učitele

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

Study Materials
The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Timetable

Tue 9:00–12:50 PUK

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Jan Kohout (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Timetable of Seminar Groups

Bi7490/1: No timetable has been entered into IS.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 9 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Timetable

Fri 16:00–17:50 kamenice

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 9 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 9 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 9 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2024

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2025

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2023

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2022

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2021

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2020

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2019

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Autumn 2018

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in spring 2018

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in autumn 2017

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2017

Extent and Intensity

1/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Autumn 2016

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course || Bi5045 Biostatistics for Comp. Biol.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2016

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2015

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Klára Komprdová, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The course is not taught in Spring 2014

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)
prof. Ing. Jiří Holčík, CSc. (lecturer)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Methods
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Special Biology (programme PřF, N-EXB)
• Special Biology (programme PřF, N-EXB, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohen's kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, cross-validation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemented with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental biology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

The course is not taught in Autumn 2007

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Jan Kohout (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

The course is not taught in Autumn 2006

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Jan Kohout (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

The course is not taught in Autumn 2005

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Jan Kohout (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 9 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

The information about the term spring 2012 - acreditation is not made public

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (lecturer)
prof. Ing. Jiří Holčík, CSc. (lecturer)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Statistical Meth.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• General Biology (programme PřF, N-BI, specialization Ekotoxikologie)

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- use classification and regression nonparametric methods
- validate the model outputs using different validation techniques
- compare results from different models
- acquisition of various software to create models(R-project, Matlab, Statistica)
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to Nonparametric Methods

• Basic concepts - process modeling, types of variables, classification model, classification x regression, parametric and nonparametric multivariate statistics - a comparison of different approaches, the introduction of various software (statistics, R-project, MATLAB)

• Decision tree I

• tree topology, criterial statistics, stability of the tree, crossvalidation, measurement of accuracy, tree pruning, surrounding variables, classification vs. regression trees, CART algorithm, the advantages and disadvantages of decision trees

• Decision tree II

• another algorithm of building tree: Patient Rule Induction Method (PRIM), Chi-squared Automatic Interaction Detector (CHAID), Quick, Unbiased and Efficient Statistical Tree (QUEST), Hierarchical Mixture of Experts (HME), Multivariate Adaptive Regression Splines (MARS)

• Random Forests I

• extension of decision trees, creation of validation of forests, different types of forests: Bagging, Boosting, Arcing

• Random Forests II

• measuring importance of variables, the effect of variables on the prediction, clustering, outlier detection, precision, prediction

• Accuracy of models I

• matrix of confusion, definition of threshold dependent and independent indexes threshold dependent indexes: Normalized Mutual Information (MI), - Average of Mutual Information (AMI), Overall Accuracy, Cohenovo kappa, Tau index

• Accuracy of models II

• threshold independent indexes, specificity x sensitivity, Receiver Operating Characteristic curve (ROC) , Area Under the ROC Curve (AUC), coefficient of determination R2, deviation D2, maximum overall accuracy MXOA, maximum kappa (MXKp), Mean cross entropy (MXE), Mean absolute prediction error (MAPE)

• Validation technique I

• validation, testing and training subsets, analytical methods for validation: Akaike's information criterion (AIC), Bayesian information criterion (BIC), Minimum description length (MDL), Structural risk minimization (SRM)

• Validation technique II

• Monte Carlo methods, principles of resampling techniques: simple splitting, crossvalidation, bootstrap and jackknife

• Real examples of using nonparametric models:

• Predictive modeling of species occurrence, concentration of pollutants

Literature

• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
• MANLY, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, Fla.: Chapman & Hall, 2007, 455 s. ISBN 9781584885412. info
• EDGINGTON, Eugene S. and Patrick ONGHENA. Randomization tests. 4th ed. Boca Raton, FL: Chapman & Hall/CRC, 2007, 345 s. ISBN 9781584885894. info

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Advanced non-parametric methods

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

Mgr. Klára Komprdová, Ph.D. (seminar tutor)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Bi5040 Biostatistics - basic course && Bi8600 Multivariate Statistical Meth.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- determine the space structure of the data
- use basic methods for spatial and predictive modeling
- acquisition of various software to create models(R-project, Matlab, Statistica)
- select an appropriate predictive method based on the distribution of data
- compare the advantages and disadvantages of different methods

Syllabus

• Introduction to predictive modelling: Principles of multivariate statistics, Comparison of parametric and nonparametric methods, Demonstration various software (STATISTIKA, R-project, MATLAB)
• Parametric regression methods (LM, GLM, GAM): Assumptions, Limitations, and Practical Considerations (selection of link function, multicolinearity, estimate parameters, residuals, deviance etc.)
• Nonparametric methods I: Decision tree: Classification and regression tree (various algorithm of building tree, accuracy, stability, crossvalidation etc.)
• Nonparametric methods II: Bagging, Boosting, Arcing, Random forest
• Spatial analysis: Interpolation and Extrapolation, Spatial autocorrelation, Pseudoreplication, using parametrical and nonparametric methods for spatial modelling
• Real examples of predicting modelling: Predictive modelling of species occurrence, concentration of pollutants; selection indicative species

Literature

• Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
• Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
• Hengl T. (2007) A Practical Guide to Geostatistical Mapping of Environmental Variables
• Lažanský et. Kol.: Umělá inteligence I.- IV.
• Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
• Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
• Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
• McCullagh, P., Nelder, J.A. (1989): Generalized Linear Models (2nd edition), Chapman & Hall
• Harrel F. E., Jr. (2001): Regression Modeling Strategies. With Applications to Linear Models, Logistic Regression and Survival Analysis. Springer, Springer Series in Statistics, New York
• Lemeshow, Stanley & Hosmer, David W., Jr.. Logistic regression, p. 1-11. In Encyclopaedia of Biostatistics, 1st ed. [Online.] Wiley, London.
• Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
• McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.

Teaching methods

Education is performed as lectures with PowerPoint presentation. Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.

Assessment methods

Final assesment (at the end of semester) is combination of written examination and project evaluation.

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

Bi7490 Introduction to Stochastic Modelling

The course is not taught in Autumn 2007 - for the purpose of the accreditation

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Gelnarová (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Jan Kohout (lecturer)
RNDr. Jan Mužík, Ph.D. (assistant)

Guaranteed by

prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.

Prerequisites

Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, regression analysis.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 10 fields of study the course is directly associated with, display

Course objectives

Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
Markov chains.
Leslie matrix.
Simple applications of regression analysis.
Estimation of optimum of environmental parameters.
Logistic regression.
Multivariate linear regression.
Generalized multivariate linear model.
Role of correlation analysis in multivariate regression.
Nonlinear regression.
Introduction to time series analysis.
Application of regression in trend analysis.
Forecasting from time series.

Syllabus

• Basic mathematical procedures with vectors and matrices, linear equations. Introduction to modelling.
• Markov chains. Applications in modelling of succession of ecosystem, structure of biological populations.
• Non - homogeneous Markov chains in ecology. Leslie matrix.
• Simple applications of regression analysis.
• Estimation of optimum of environmental parameters. Gaussian curves. Indicator species values.
• Logistic regression - one- and multivariate model.
• Multivariate linear regression. The least square method. The maximum likehood method.
• Generalized multivariate linear model. Analysis of residuals - homoscedacity. Autocorrelation.
• Role of correlation analysis in multivariate regression. Multicolinearity.
• Nonlinear regression.
• Modelling using contingency tables in ecology.
• Introduction to time series analysis. Autocorrelation. Trend analysis. Non-parametric methods for estimation of trends.
• Application of regression in trend analysis. Polynomial regression.
• Box-Jwenkins modelling. Spline methods. Forecasting from time series.

Literature

• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
• Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
• HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
• MCCULLAGH, P. and John A. NELDER. Generalized linear models. 2nd ed. London: Chapman & Hall, 1989, xix, 511. ISBN 0412317605. info
• Cajo J.F. ter Braak, (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
• SOKAL, Robert R. and James F. ROHLF. Biometry :the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887 s. ISBN 0-7167-2411-1. info

Language of instruction

Czech

Further Comments

The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.

Teacher's information

http://www.cba.muni.cz/vyuka/

• Enrolment Statistics (recent)