Course detail

Machine Learning

FEKT-MPC-STUAcad. year: 2023/2024

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)

Entry knowledge

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

Rules for evaluation and completion of the course

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

Aims

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

Study aids

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. Tomáš Zemčík
Ing. Karel Horák, Ph.D.
Ing. Dominik Řičánek
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.