Course detail

Information Representation and Machine Learning

FEKT-DKA-IMLAcad. year: 2025/2026

Complexity theory, graph theory, graph equivalence, queuing theory, Petri nets, simulation and modeling, Markov models, advanced evolutionary algorithms.

Language of instruction

English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Radim Burget, Ph.D.

Department

Department of Telecommunications (UTKO)

Offered to foreign students

The home faculty only

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

final examination

Aims

Objective of this course is to provide information about complexity theeory, graph theory and their comparison, queuing theory, Petri nets, evolution algorithms.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Goldreich, Oded. "Computational complexity: a conceptual perspective." ACM SIGACT News 39.3 (2008): 35-39. (EN)

Recommended reading

Bürgisser, Peter, Michael Clausen, and Amin Shokrollahi. Algebraic complexity theory. Vol. 315. Springer Science & Business Media, 2013. (EN)
Mitleton-Kelly, Eve. Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. Elsevier Science Ltd, 2003. (EN)

Classification of course in study plans

  • Programme DKA-EIT Doctoral 0 year of study, summer semester, compulsory
  • Programme DKAD-EIT Doctoral 0 year of study, summer semester, compulsory

Type of course unit

 

Guided consultation

39 hod., optionally

Teacher / Lecturer

doc. Ing. Radim Burget, Ph.D.

Syllabus

L01: Complexity Theory 
L02: Selected complexity problems
L03: Strongly Connected Components
L04: Graph Theory
L05: Pairing and izomorphism
L06: Flow and Cuts in Graphs
L07: Neural Networks
L08: Convolutional NN
L09: Basics of Machine Learning
L10: Recurent NN
L11: Reinforcement learning
L12: NN for trees and graphs
L13: Summary and preparation for final exam