Course detail

Neural networks and Machine Learning

FSI-VSCAcad. year: 2025/2026

The course provides an introduction to the theory and methods of machine learning, focusing on their application in solving classification, regression, and clustering tasks.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Pavel Škrabánek, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

Entry knowledge

Basic knowledge of statistics, optimization, and programming is expected.

Rules for evaluation and completion of the course

Knowledge and skills are verified by credit and examination. Credit requirements: elaboration of given tasks. Attendance at lectures is recommended, while attendance at practical sessions is mandatory. Practical sessions that a student is unable to attend in the regular term can be made up during a substitute term. The exam is oral and covers the entire course material.

Aims

The aim of the course is to familiarize students with machine learning methods and their applications in classification, regression, and clustering. Students will learn about both parametric and non-parametric classification and regression models, as well as key concepts such as error metrics, regularization, cross-validation, gradient descent, and modern approaches, including boosting and Gaussian mixture models. The course bridges theory and practice, focusing on the design and implementation of machine learning models.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

ALPAYDIN, Ethem. Introduction to machine learning. Third edition. Adaptive computation and machine learning. Cambridge: The MIT Press, [2014]. ISBN 978-0-262-02818-9. (EN)
BISHOP, Christopher M. Pattern recognition and machine learning. Information science and statistics. New York: Springer, c2006. ISBN 978-0-387-31073-2. (EN)
Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 80-85863-18-9 (CS)

Recommended reading

B. Kosko: Neural Networks and fuzzy systems. Prentice Hall 1992 (EN)
Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8. (EN)

Classification of course in study plans

  • Programme N-AIŘ-P Master's 1 year of study, summer semester, compulsory

  • Programme C-AKR-P Lifelong learning

    specialization CLS , 1 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Pavel Škrabánek, Ph.D.

Syllabus

  1. Introduction, supervised and unsupervised learning, regression vs. classification, dataset splitting, error metrics, loss functions, cross-validation, overfitting, regularization.
  2. Linear regression, least squares method, gradient descent, regularized least squares method.
  3. Linear classification, logistic regression, regularized logistic regression.
  4. Non-parametric models, nearest neighbors method.
  5. Decision trees for classification and regression problems.
  6. Generative models for classification, Bayes classifier, Gaussian discriminant analysis.
  7. Naive Bayes classifier.
  8. Support vector machines, kernel functions.
  9. Clustering, k-means clustering.
  10. Gaussian mixture models.
  11. Dimensionality reduction, boosting.
  12. Mathematical model of a neuron, activation functions, multilayer perceptron, forward and backward propagation.
  13. Feedforward and multilayer neural networks, recurrent networks, topologically organized neural networks.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Pavel Škrabánek, Ph.D.

Syllabus

  1. Introduction to the programming environment.
  2. Least squares method and regularized least squares method.
  3. Linear classification, logistic regression, regularized logistic regression.
  4. Nearest neighbors method.
  5. Decision trees for classification and regression problems.
  6. Bayes classifier, Gaussian discriminant analysis.
  7. Naive Bayes classifier.
  8. Support vector machines.
  9. K-means clustering.
  10. Gaussian mixture models.
  11. Dimensionality reduction, boosting.
  12. Multilayer perceptron.
  13. Final assessment.