Course detail

Machine Learning

FEKT-MSTUAcad. year: 2018/2019

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.

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


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


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Mitchell, Tom M. Machine learning. Boston : McGraw-Hill, 1997. 414 s. McGraw-Hill series in computer science. ISBN 0-07-042807-7. (EN)
Honzík P.: Strojové učení. Elektonická skripta VUT. (CS)

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.

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Machine learning paradigms and terminology.
2. Statistics in machine learning.
3. Basics of information theory.
4. Decision trees.
5. Instance based learning.
6. Loss functions.
7. Model performance estimation.
8. Pre-processing.
9. Bayesian learning.
10. Genetic algorithms.
11. Linear regression. Discriminant analysis. Support vector machines.
12. Meta learning.
13. Unsupervised learning.


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.

Classification of course in study plans

  • Programme EEKR-M1 Master's

    branch M1-KAM , 2. year of study, winter semester, 5 credits, optional specialized

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, winter semester, 5 credits, optional specialized

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer