Course Information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2025

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Bc. Terézia Mikulová (assistant)
RNDr. Ondřej Sotolář (assistant)
Bc. Jonáš Tichý (assistant)

Guaranteed by

Prerequisites

A student needs to be familiar with basics of machine learning (e.g. IB031 Introduction to machine learning).

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 37 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Advanced machine learning methods. Ensemble learning. AutoML, preference learning. Multi-relational learning.
Mining frequent patterns and association rules. Apriori algorithm. Frequent patterns in multi-relational data.
Anomaly analysis.
Preprocessing. Feature selection and construction. Sampling
Active learning. Semi-supervised learning
Temporal data mining.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

not specified

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
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

English

Further Comments

The course is taught annually.
The course is taught: every week.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2023

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Ondřej Sotolář (assistant)

Guaranteed by

Timetable

Wed 15. 2. to Wed 10. 5. Wed 14:00–15:50 D1

Prerequisites

A student needs to be familiar with basics of machine learning (e.g. IB031 Introduction to machine learning).

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 74 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

not specified

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
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2022

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Faruk Herenda (assistant)
Mgr. Ján Krčmář (assistant)

Guaranteed by

Timetable

Wed 16. 2. to Wed 11. 5. Wed 8:00–9:50 D1

Prerequisites

A student needs to be familiar with basics of machine learning (e.g. IB031 Introduction to machine learning).

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 74 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

not specified

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
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2021

Extent and Intensity

2/0/1. 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. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Radoslav Doktor (assistant)

Guaranteed by

Timetable

Wed 8:00–9:50 Virtuální místnost

Prerequisites

A student needs to be familiar with basics of machine learning (e.g. IB031 Introduction to machine learning).

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 74 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

A student will be able
- to pre-process data for data mining;
- to know advanced method of machine learning and data mining and to use them;
- to write a technical report;
- build and validate an advanced machine learning/data mining method.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info

not specified

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

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2020

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Dušan Hetlerović (assistant)
Mgr. Katarína Nocarová (assistant)

Guaranteed by

Timetable

Mon 17. 2. to Thu 7. 5. Wed 8:00–9:50 A318; and Wed 13. 5. 8:00–9:50 B517

Prerequisites

A student needs to be familiar with basics of machine learning (e.g. IB031 Introduction to machine learning).

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 74 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info

not specified

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

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2019

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Róbert Kolcún (assistant)
Mgr. Ondrej Kurák (assistant)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.
Supplier department: Department of Computer Science – Faculty of Informatics

Timetable

Wed 16:00–17:50 B410

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 37 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2018

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Veronika Krejčířová (assistant)
RNDr. Karel Vaculík, Ph.D. (assistant)

Guaranteed by

Timetable

Wed 8:00–9:50 C525

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 37 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Learning outcomes

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2017

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Tomáš Rudolecký (assistant)
Mgr. Jan Sedlák (assistant)
RNDr. Karel Vaculík, Ph.D. (assistant)

Guaranteed by

Timetable

Tue 14:00–15:50 B410

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 37 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2016

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Karel Vaculík, Ph.D. (assistant)

Guaranteed by

Timetable

Thu 8:00–9:50 C511

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 37 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing.
Mining frequent patterns and association rules.
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

recommended literature

HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info

Teaching methods

Lectures, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2015

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Karel Vaculík, Ph.D. (assistant)

Guaranteed by

Timetable

Thu 14:00–15:50 C511

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 36 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Inductive query languages.
PMML
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Teaching methods

Lectures, exercises, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2014

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Juraj Jurčo (assistant)
RNDr. Karel Vaculík, Ph.D. (assistant)

Guaranteed by

Timetable

Tue 8:00–9:50 G124

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 36 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Inductive query languages.
PMML
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Teaching methods

Lectures, exercises, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2013

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Juraj Jurčo (assistant)
RNDr. Mgr. Jaroslav Bayer (assistant)
RNDr. Hana Bydžovská, Ph.D. (assistant)
RNDr. Jan Géryk, Ph.D. (assistant)

Guaranteed by

Timetable

Wed 12:00–13:50 B410

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 39 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Inductive query languages.
PMML
Data visualization, visual analytics
Text mining, mining in spatio-temporal dat, web mining.
Data mining and life sciences.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Teaching methods

Lectures, exercises, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2012

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Petr Kosina, Ph.D. (assistant)
RNDr. Jan Géryk, Ph.D. (assistant)
Mgr. Juraj Jurčo (seminar tutor)

Guaranteed by

Timetable

Tue 8:00–9:50 B410

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 39 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Inductive query languages.
PMML
Text mining, mining in spatio-temporal dat, web mining.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Teaching methods

Lectures, exercises, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2011

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Petr Glos (assistant)
Mgr. Jana Kadlecová (assistant)
Mgr. Jan Knotek (assistant)
Georg Schroeder (assistant)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Mon 14:00–15:50 B410

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 38 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Inductive query languages.
PMML
Text mining, mining in spatio-temporal dat, web mining.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Teaching methods

Lectures, exercises, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2010

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Georg Schroeder (assistant)
Bc. Jakub Tischler (assistant)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Mon 16:00–17:50 B204

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 36 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.

Syllabus

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Knowledge management. Inductive query languages. PMML
Text mining, mining in spatio-temporal dat, web mining.
Data mining, data warehousing, OLAP.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Teaching methods

Lectures, exercises, a project.

Assessment methods

Written and oral exam. A defense of a project is as a part of the exam.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2009

Extent and Intensity

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

Teacher(s)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Wed 10:00–11:50 B204

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 37 fields of study the course is directly associated with, display

Course objectives

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.

Syllabus

Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Knowledge management. Inductive query languages. PMML
Text mining, mining in spatio-temporal dat, web mining.
Data mining, data warehousing, OLAP.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info

Assessment methods

A project is as a part of the course.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Data Mining and Knowledge Discovery

Faculty of Informatics
Spring 2008

Extent and Intensity

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

Teacher(s)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Thu 16:00–17:50 B007

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 37 fields of study the course is directly associated with, display

Course objectives

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.

Syllabus

Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Knowledge management. Inductive query languages. PMML
Text mining, mining in spatio-temporal dat, web mining.
Data mining, data warehousing, OLAP.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Data Mining and Knowledge Discovery

Faculty of Informatics
Spring 2007

Extent and Intensity

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

Teacher(s)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Thu 16:00–17:50 B011

Prerequisites (in Czech)

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 17 fields of study the course is directly associated with, display

Course objectives

Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.

Syllabus

Knowledge discovery in databases. Data mining.
Basic algorithms of machine learning.
Preprocessing. Active learning.
Mining frequent patterns and association rules.
Knowledge management. Inductive query languages. PMML
Text mining, mining in spatio-temporal dat, web mining.
Data mining, data warehousing, OLAP.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2006

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Thu 14:00–15:50 B007

Prerequisites (in Czech)

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 18 fields of study the course is directly associated with, display

Course objectives

Intrduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.

Syllabus

Knowledge, association, dependency in databases. Interestingness relation. Knowledge discovery in databases(KDD). Data mining.
Typical KDD tasks: clustering, classification, dependency discovery, deviation detection.
Basic algorithms of machine learning.
Preprocessing.
Association rules
KDD systems MineSet and Kepler.
DBMS extension to support KDD. KESO Project.
Inductive query languages. DBLearn.
Knowledge discovery in RDB, OODB, geographic data and WWW and text.
Data warehousing, OLAP.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2005

Extent and Intensity

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

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Jan Blaťák, Ph.D. (seminar tutor)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Thu 16:00–17:50 B410, Thu 18:00–18:50 B001

Prerequisites (in Czech)

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 18 fields of study the course is directly associated with, display

Course objectives

Intrduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.

Syllabus

Knowledge, association, dependency in databases. Interestingness relation. Knowledge discovery in databases(KDD). Data mining.
Typical KDD tasks: clustering, classification, dependency discovery, deviation detection.
Basic algorithms of machine learning.
Preprocessing.
Association rules
KDD systems MineSet and Kepler.
DBMS extension to support KDD. KESO Project.
Inductive query languages. DBLearn.
Knowledge discovery in RDB, OODB, geographic data and WWW and text.
Data warehousing, OLAP.

Literature

BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. info
Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2004

Extent and Intensity

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

Teacher(s)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Mon 16:00–17:50 B011

Prerequisites (in Czech)

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 8 fields of study the course is directly associated with, display

Course objectives (in Czech)

Přehled základních metod, algoritmů a systémů pro vyhledávání znalostí v databázích. Součástí předmětu je projekt.

Syllabus

Knowledge, association, dependency in databases. Interestingness relation. Knowledge discovery in databases(KDD). Data mining.
Typical KDD tasks: clustering, classification, dependency discovery, deviation detection.
Basic algorithms of machine learning.
Preprocessing.
Association rules
KDD systems MineSet and Kepler.
DBMS extension to support KDD. KESO Project.
Inductive query languages. DBLearn.
Knowledge discovery in RDB, OODB, geographic data and WWW and text.
Data warehousing, OLAP.

Literature

Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information

The course is also listed under the following terms Spring 2003, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

PV056 Knowledge Discovery in Databases

Faculty of Informatics
Spring 2003

Extent and Intensity

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

Teacher(s)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.

Timetable

Mon 14:00–15:50 B011

Prerequisites (in Czech)

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 8 fields of study the course is directly associated with, display

Course objectives (in Czech)

Přehled základních metod, algoritmů a systémů pro vyhledávání znalostí v databázích. Součástí předmětu je projekt.

Syllabus

Knowledge, association, dependency in databases. Interestingness relation. Knowledge discovery in databases(KDD). Data mining.
Typical KDD tasks: clustering, classification, dependency discovery, deviation detection.
Basic algorithms of machine learning.
Preprocessing.
Association rules
KDD systems MineSet and Kepler.
DBMS extension to support KDD. KESO Project.
Inductive query languages. DBLearn.
Knowledge discovery in RDB, OODB, geographic data and WWW and text.
Data warehousing, OLAP.

Literature

Advances in knowledge discovery and data mining. Edited by Usama M. Fayyad. Menlo Park: AAAI Press, 1996, xiv, 611. ISBN 0262560976. info

Assessment methods (in Czech)

Nutnou podmínkou absolvování je projekt.

Language of instruction

Czech

Further Comments

The course is taught annually.

Listed among pre-requisites of other courses

PA026 Artificial Intelligence Project
PB016 || IV126 || PV021 || PV056

Teacher's information