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.

Elearning

eLearning: currently opened course

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 hours, optionally

Teacher / Lecturer

doc. Ing. Radim Kolář, Ph.D.
Ing. Jiří Chmelík, Ph.D.
Ing. Tomáš Vičar, Ph.D.

Syllabus

1) Introduction to machine learning, mathematical background, optimization in machine learning.
2) Model evaluation - supervised pipeline, validation approaches, metrics, preprocessing.
3) Dimensionality reduction - feature selection and feature reduction methods.
4) Linear models - definition, losses and regularizations, classification models, regression models.
5) Bias-variance tradeoff, decision trees and random forests, bagging and boosting.
6) Basics of neural networks - simple neural network, activation functions, loss functions, regularization, optimization methods.
7) Principles of deep learning - deep neural networks (NN) and basic building blocks.
8) Principles of deep NN - special blocks.
9) Architectures and applications of deep NN - regression, classification, image2image and signal2signal.
10) Transformers - attention mechanism, tokenizations, vision transformers, language models.
11) Probabilistic models basics - probability distributions, maximum likelihood estimation, maximum a-posteriori estimation.
12) Probabilistic models - Naive Bayes Classifier, Gaussian mixture model, Logistic regression. 

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

Ing. Tomáš Vičar, Ph.D.
Ing. Kateřina Krejčí
Ing. et Ing. Michal Nohel

Syllabus

During computer exercises, students practically implement machine learning algorithms in Python using scikit-learn, PyTorch, and other libraries. Exercise topics are as follows:
1) Introduction to machine learning in Python, simple classifier example, introduction to scikit-learn.
2) Model evaluation methods – metrics, model validation.
3) Dimensionality reduction - introduction to pandas, feature selection and PCA.
4) Linear models - linear and polynomial regression, LASSO/RIDGE regression, classification with linear models.
5) Dual forms and kernels - regression, SVM.
6) Decision trees, random forest and boosting.
7) Artificial neural network introduction, introduction to PyTorch.
8) Practical deep learning example - image classification deployed on smartphone.
9) Deep learning in various applications – image classification, signal classification, image segmentation, image2image regression, signal segmentation, 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, Logistic regression.
 

Elearning

eLearning: currently opened course