# FI:IB031 Intro to Machine Learning - Course Information

## IB031 Introduction to Machine Learning

**Faculty of Informatics**

Spring 2021

**Extent and Intensity**- 2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).

Taught online. **Teacher(s)**- doc. RNDr. Tomáš Brázdil, Ph.D. (lecturer)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)

Bc. Aleš Calábek (seminar tutor)

RNDr. Jaroslav Čechák, Ph.D. (seminar tutor)

RNDr. Tomáš Effenberger, Ph.D. (seminar tutor)

Mgr. Adam Ivora (seminar tutor)

Bc. Marek Kadlčík (seminar tutor)

RNDr. Filip Lux (seminar tutor)

doc. Mgr. Bc. Vít Nováček, PhD (seminar tutor)

Bc. Michal Starý (seminar tutor) **Guaranteed by**- doc. RNDr. Tomáš Brázdil, Ph.D.

Department of Machine Learning and Data Processing – Faculty of Informatics

Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics **Timetable**- Tue 8:00–9:50 Virtuální místnost
- Timetable of Seminar Groups:

*J. Čechák, T. Effenberger*

IB031/02: Mon 16:00–17:50 Virtuální místnost,*F. Lux, M. Starý*

IB031/03: Tue 14:00–15:50 Virtuální místnost,*A. Calábek, J. Čechák*

IB031/04: Mon 14:00–15:50 Virtuální místnost,*M. Kadlčík, V. Nováček*

IB031/05: Tue 18:00–19:50 Virtuální místnost,*F. Lux* **Prerequisites**- Recommended courses are MB102 a MB103.
**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 40 fields of study the course is directly associated with, display
**Course objectives**- By the end of the course, students should know basic methods of machine learning and understand their basic theoretical properties, implementation details, and key practical applications. Also, students should understand the relationship among machine learning and other sub-areas of mathematics and computer science such as statistics, logic, artificial intelligence and optimization.
**Learning outcomes**- By the end of the course, students

- will know basic methods of machine learning;

- will understand their basic theoretical properties, implementation details, and key practical applications;

- will understand the relationship among machine learning and other sub-areas of mathematics and computer science such as statistics, logic, artificial intelligence and optimization;

- will be able to implement and validate a simple machine learning method. **Syllabus**- Basic machine learning: classification and regression, clustering, (un)supervised learning, simple examples
- Decision trees: learning of decision trees and rules
- Logic and machine learning: specialization and generalization, logical entailment
- Evaluation: training and test sets, overfitting, cross-validation, confusion matrix, learning curve, ROC curve; sampling, normalisation
- Probabilistic models: Bayes rule, MAP, MLE, naive Bayes; introduction to Bayes networks
- Linear regression (classification): least squares, relationship wih MLE, regression trees
- Kernel methods: SVM, kernel transformation, kernel trick, kernel SVM
- Neural networks: multilayer perceptron, backpropagation, non-linear regression, bias vs variance, regularization
- Lazy learning: nearest neighbor method; Clustering: k-means, hierarchical clustering, EM
- Practical machine learning: Data pre-processing: attribute selection and construction, sampling. Ensemble methods. Bagging. Boosting. Tools for machine learning.
- Advanced methods: Inductive logic programming, deep learning.

**Literature**- MITCHELL, Tom M.
*Machine learning*. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info

*recommended literature*- GÉRON, Aurélien.
*Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems*. Second edition. Beijing: O'Reilly, 2019, xxv, 819. ISBN 9781492032649. info - ROGERS, Simon and Mark GIROLAMI.
*A first course in machine learning*. Boca Raton: CRC Press/Taylor & Francis Group, 2012, xx, 285. ISBN 9781439824146. info - FLACH, Peter A.
*Machine learning : the art and science of algorithms that make sense of data*. New York: Cambridge University Press, 2012, xvii, 396. ISBN 1107422221. info *Pattern recognition and machine learning*. Edited by Christopher M. Bishop. New York: Springer, 2006, xx, 738. ISBN 0387310738. info- BERKA, Petr.
*Dobývání znalostí z databází*. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info

*not specified*- MITCHELL, Tom M.
**Bookmarks**- https://is.muni.cz/ln/tag/FI:IB031!
**Teaching methods**- Lectures + practical exercises + project
**Assessment methods**- Intrasemestral exam, project, final exam.
**Language of instruction**- Czech
**Follow-Up Courses****Further Comments**- Study Materials

The course is taught annually.

- Enrolment Statistics (Spring 2021, recent)
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