Course detail

Machine Learning

FEKT-MPA-MLRAcad. year: 2025/2026

Students will gain insight into advanced machine learning methods. They will be able to describe and compare the properties of individual approaches to data classification. They will be able to select and apply a specific approach to a given problem. They will also gain practical experience with current implementations of machine learning methods including deep learning.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Tomáš Vičar, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Offered to foreign students

Of all faculties

Entry knowledge

- Overview of basic concepts of machine learning.

- Basic knowledge of programming, preferably in Python.

- Mathematical foundations – linear algebra (matrices, vectors), basics of differential calculus and probability.

- Fundamentals of statistics a optimization.

 

Rules for evaluation and completion of the course

A maximum of 30 points can be obtained during the semester. A maximum of 70 points can be obtained in the final examination.

There will be 6 tests during the semester (each test for maximum of 5 points). The tests cannot be repeated.

The conditions for the award of credit are as follows:
- full participation in the computer labs (max. two excused absences),
- obtaining at least 15 points from the tests.

Obtaining credit is a condition for admission to the final examination.

The final exam will be marked with a maximum of 70 points. A minimum of 35 points is required to pass the exam.

Aims

The course aims to broaden students' understanding of advanced machine learning techniques. Participants will develop the ability to describe, analyze, and differentiate between various data classification methods. They will learn how to effectively select and implement appropriate techniques for specific problems. Furthermore, the course provides hands-on experience with the latest machine learning tools, including deep learning, enhancing their practical skillset in this field. 

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Deisenroth,M.P, Faisal, A.A, Ong, Ch.S.:Mathematics for Machine Learning, Cambridge University Press, 2020 (EN)
Geron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2. edition, O'Reilly Media (EN)
Ch. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2011
I. Goodfellow, Y. Bengio, A. Courville, F. Bach: Deep Learning, The MIT Press, 2016
N. Buduma: Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme MPA-BTB Master's 2 year of study, winter semester, compulsory
  • Programme MPAD-BIO Master's 1 year of study, winter semester, compulsory
  • Programme MPC-BTB Master's 1 year of study, winter semester, compulsory
  • Programme MPC-BIO Master's 1 year of study, winter semester, compulsory
  • Programme MPA-BIO Master's 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Tomáš Vičar, Ph.D.

Syllabus

1. Introduction to the issue of classification. Evaluation of classifiers, classification error, testing of classifiers.

2. Feature assessment, selection, and reduction using basic and advanced methods (PCA, mRMR, t-SNE).

3. Linear classifiers – basic principles and methods (perceptron, MSE, SVM).

4. Kernel approach for non-linear classification/regression.

5. Decision and regression trees and forests, random forests.

6. Methods for improving classifier properties (bagging, boosting).

7. Basic principles of artificial neural networks, regularization techniques.

8. Principles of deep learning, deep neural networks (NN) and basic building blocks.

9. Principles of deep NN learning, convolutional NNs, blocks used in deep NNs.

10. Variants of deep NNs, recurrent networks, transformers.

11. Probabilistic models, Methods “Maximum likelihood” and “Maximum a-posteriori probability”.

 

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Tomáš Vičar, Ph.D.

Syllabus

1) Introduction to machine learning in pyton, simple classifier example
2) Model evaluation methods – metrics, model validation
3) Linear and polinomial regression, LASSO/RIDGE regression
4) Dual forms and kernels - regression, SVM
5) Feature selection - feature filtering, feature wrapping, forward method
6) Decision trees, random forest, bias-variance trade-off
7) Artificial neural network introduction, introduction to PyTorch
8) Introduction to PyTorch 2, simple neural network
9) Deep learning in various applications – image classification, signal classification, image segmentation, image2image regression, signal segmentation, signal2signal regression, signal2signal regression
10) Transformers - vision transformer example, next word prediction example
11) Probabilistic models 1 - Maximum likelihood estimation, Maximum a-posteriori estimation
12) Probabilistic models 2 - Naive Bayes Classifier, Gaussian mixture model