Course detail

Machine Learning

FEKT-MPC-STUAcad. year: 2021/2022

The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. The goal of the subject is to present the key algorithms and theory that form the core of machine learning. Machine learning is mathematical-logical base in many fields including artificial intelligence, pattern recognition or data mining. The main attention is given on classification and optimization tasks.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Václav Jirsík, CSc.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

The graduate is able to
- design own solution of a classification task
- pre-process data, including feature selection
- estimate quality of selected model
- justify rightness of suggested solution
- design own solution of optimization task
- select appropriate search heuristic for given problem

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested namely in mathematics, statistics and probability theory.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Techning methods include lectures and computer laboratories. Students have to write a single project during the course.

Assesment methods and criteria linked to learning outcomes

A group project (40 pts) and a final exam (60 pts) are evaluated during the Machine Learning course. For successful pass the course, obtaining of at least half of available points is required in both mentioned parts.

Course curriculum

1. Machine learning paradigms. Terminology. Concept learning. Basics of information theory.
2. Statistics in machine learning.
3. Instance based learning.
4. Decision trees.
5. Model performance estimation.
6. Loss functions. Pre-processing 1.
7. Pre-processing 2.
8. Genetic algorithms. Differential evolution. Ant colony optimization.
9. Bayesian learning.
10. Linear regression. Discriminant analysis. Support vector machines.
11. Meta learning, ensemble methods.
12. Unsupervised learning.

Work placements

Not applicable.

Aims

The aim are large knowledge in machine learning with emphasise on classification and optimisation tasks.

Specification of controlled education, way of implementation and compensation for absences

Only registration and submitting of the project are obligatory to qualify for examination.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Honzík P.: Strojové učení. Elektonická skripta VUT. (CS)

Recommended reading

Mitchell, Tom M. Machine learning. Boston : McGraw-Hill, 1997. 414 s. McGraw-Hill series in computer science. ISBN 0-07-042807-7. (EN)

Classification of course in study plans

  • Programme MPC-KAM Master's 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Syllabus

1. Machine learning paradigms. Terminology. Concept learning. Basics of information theory.
2. Statistics in machine learning.
3. Instance based learning.
4. Decision trees.
5. Model performance estimation.
6. Loss functions. Pre-processing 1.
7. Pre-processing 2.
8. Genetic algorithms. Differential evolution. Ant colony optimization.
9. Bayesian learning.
10. Linear regression. Discriminant analysis. Support vector machines.
11. Meta learning, ensemble methods.
12. Unsupervised learning.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Lukáš Kratochvíla
Ing. Tomáš Zemčík
Ing. Karel Horák, Ph.D.
Ing. Šimon Bilík

Syllabus

1. Machine learning paradigms. Terminology. Concept learning. Basics of information theory.
2. Statistics in machine learning.
3. Instance based learning.
4. Decision trees.
5. Model performance estimation.
6. Loss functions. Pre-processing 1.
7. Pre-processing 2.
8. Genetic algorithms. Differential evolution. Ant colony optimization.
9. Bayesian learning.
10. Linear regression. Discriminant analysis. Support vector machines.
11. Meta learning, ensemble methods.
12. Unsupervised learning.