Course detail

Machine Learning

FEKT-MPA-MLRAcad. year: 2020/2021

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.

Learning outcomes of the course unit

Not applicable.


Not applicable.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

N. Buduma: Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)
Ch. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2011
I. Goodfellow, Y. Bengio, A. Courville, F. Bach: Deep Learning, The MIT Press, 2016

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Introduction to classification. Classification error, classifier testing.
2. Features assessment, feature selection and feature reduction with basic and advanced methods (PCA, mRMR, t-SNE).
3. Linear Classifiers - basic principles and methods (perceptron, SVM, MSE).
4. Kernel approach for non-linear classification.
5. Bayesian approach to classification. Naive Bayes classifier.
6. Maximum likelihood and Maximum a-posteriori probability.
7. Decision and regression trees and forests, random forests.
8. Methods for improving classifier properties (bagging, boosting).
9. Basics of neural networks, regularization.
10. Principles of deep learning, deep neural networks (NN) and basic building blocks.
11. Principles of deep NN learning.
12. Variants of deep NN, autoencoders, recurrent NN, LSTM, GRU, GAN.
13. Application of classification tasks for processing of signals, images and bioinformatic data. Application examples.


Not applicable.

Classification of course in study plans

  • Programme MPA-BIO Master's, 2. year of study, winter semester, 5 credits, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer