Course detail

Artificial Intelligence

FEKT-MPC-UINAcad. year: 2025/2026

The course provides a systematic introduction to the field of artificial intelligence, covering a broad spectrum from classical symbolic methods through evolutionary algorithms to modern neural networks. Students will gain an overview of the fundamental principles of AI and will learn to work with state space search, game-playing algorithms, fuzzy logic, and bio-inspired approaches.

The second half of the course focuses on artificial neural networks (ANNs), which today represent a major direction in AI development. The curriculum covers everything from foundational models (perceptron, ADALINE), through topologically organized networks (SOM, RBF), to deep neural networks, convolutional and recurrent architectures, autoencoders, generative models, and large language models. The course enables students to understand how neural networks function, their areas of application, and their inherent limitations.

Graduates of the course will acquire a comprehensive and up-to-date overview of the tools, principles, and trends in artificial intelligence, as well as the ability to navigate the rapidly evolving AI landscape.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Václav Jirsík, CSc.

Department

Department of Control and Instrumentation (UAMT)

Entry knowledge

The subject knowledge on the Bachelor´s degree level is requested and knowledge about programming MATLAB.

Rules for evaluation and completion of the course

The following requirements must be fulfilled in order to receive credit for the course:

  • 100% attendance in the compulsory part of the instruction. Computer lab sessions are mandatory; any missed session, if properly excused, may be made up upon agreement with the instructor.

  • Completion of two online training courses, each confirmed by a certificate, worth 5 points each (total of 10 points).

  • Completion of two projects (with the assignment developed in collaboration with the student), each earning a minimum of 5 points and up to a maximum of 20 points. Each project must be completed and defended within the specified time frame. The maximum number of points for projects is 40, which will be added to the final exam score.

The final exam consists of both written and oral parts, with a maximum of 60 points. A score below 30 points is considered a failure. The final grade is determined by the sum of the exam points and the points earned during the exercises.

Aims

The aim of the course is to introduce students to selected methods and approaches in artificial intelligence (AI), covering both classical symbolic AI (GOFAI) and modern subsymbolic AI, with particular emphasis on neural networks. The objective of the course is thus to clarify the theoretical foundations of the discussed AI methods, to understand their implementation and application possibilities, and to develop the ability to critically evaluate the suitability of their use in practice.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

MAŘÍK, Vladimír, ŠTĚPÁNKOVÁ, Olga, LAŽANSKÝ, Jiří a kolektiv. Umělá inteligence (1. až 6. díl) Praha: Academia 1993 - 2013. (CS)
RUSESELL, Stuart a NORVIG, Peter. Artificial Intelligence. A Modern Aproach. New Jersey: Prentice Hall 2010. 1132 s. ISBN-13: 978-0-13-604259-4. (EN)

Recommended reading

DUDA, Richard, HART Peter a STORK David. Pattern Classification. New York: John Wiley & Sons, INC. 2001. 654 s. ISBN 0-471-05669-3. (CS)
SONKA, Milan, HLAVAC, Vaclav a BOYLE, Rogert. Image Processing, Analysis and Machine Vision. Toronto: Thomson, 2008. 829 s. ISBN 978-0-495-24438-7. (CS)

Classification of course in study plans

  • Programme MPC-AUD Master's

    specialization AUDM-TECH , 1 year of study, winter semester, compulsory-optional
    specialization AUDM-ZVUK , 1 year of study, winter semester, compulsory-optional

  • Programme MPC-EAK Master's 0 year of study, winter semester, elective
  • Programme MPC-EEN Master's 0 year of study, winter semester, elective
  • Programme MPC-IBE Master's 2 year of study, winter semester, compulsory-optional
  • Programme MPC-TIT Master's 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Václav Jirsík, CSc.
prof. Ing. Radomil Matoušek, Ph.D.

Syllabus

  1. Artificial Intelligence (AI): Taxonomy, Philosophy, Law, and the Future.
  2. State Space. Uninformed and Informed Search.
  3. Game Playing Methods. Min/Max Algorithm.
  4. Fuzzy Approaches and Expert Systems.
  5. Bio-Inspired Algorithms: GA, DE, PSO, and Swarm Intelligence. [Project A info]
  6. Bio-Inspired Algorithms and Combinatorial Optimization, Surrogate Models.
  7. Artificial Neural Networks (ANN) – Paradigms, Perceptron, ADALINE.
  8. Topologically Organized ANNs (SOM, Kohonen Networks) and RBF Networks – Paradigms.
  9. Deep Neural Networks (DNN) – Paradigms, MLP, RBM, Backpropagation, etc. Autoencoders and Their Applications.
  10. DNN – Convolutional Neural Networks (CNN), Multi-Class Classification. [Project B info]
  11. DNN – Recurrent Neural Networks (RNN, LSTM, GRU), Encoder-Decoder Architectures.
  12. DNN – Generative Neural Network Models. LLMs. Transformers.
  13. SNN, GNN (Graph Neural Networks), Physics-Informed Neural Network Models. Course colloquium.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Václav Jirsík, CSc.
prof. Ing. Radomil Matoušek, Ph.D.

Syllabus

  1. Lab: Python Basics, MATLAB Capabilities (Programming Basics).
  2. Lab: A in a Simple Maze vs. BFS/DFS. 
  3. Lab: Min/Max and Alpha-Beta Pruning, Tic-Tac-Toe.
  4. Lab: Fuzzy Inference System (MATLAB). 
  5. Lab: GA, DE, PSO: Global Optimization (Benchmarking, Controller Design). [Project A]
  6. Lab: GA, HC12: Combinatorial Optimization (TSP).
  7. Lab: [Project A – defense]
  8. Lab: Linear Classifier, Perceptron, XOR and EA Learning.
  9. Lab: Simple Clustering (SOM).
  10. Lab: Data Approximation (Regression). [Project B]
  11. Lab: (Keras/PyTorch) Classification 1/2 (MNIST).
  12. Lab: (Keras/PyTorch) Classification 2/2 (ImageNet).
  13. Lab: [Project B – defense].