Course detail

Data Structures and Algorithms

FEKT-MPC-PDAAcad. year: 2023/2024

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

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

The subject knowledge on the heoretical informatics, t Bachelor degree and courlevel is required.

Rules for evaluation and completion of the course

final examination
The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.


Objective of this course is to provide information about complexity theeory, graph theory and their comparison, queuing theory, Petri nets, evolution algorithms.
Alumni know complexity theory, representative examples and are able to apply graph theory, queue theory, theory of Petri nets and Markov models to solve the selected examples.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Virius, Miroslav. Základy algoritmizace. Česká technika-nakladatelství ČVUT, 2008. (CS)
GOODFELLOW, I., BENGIO, Y., & COURVILLEe, A. (2016). Deep learning (adaptive computation and machine learning series). Adaptive Computation and Machine Learning series, 800. (EN)

Recommended reading

Not applicable.


Classification of course in study plans

  • Programme MPC-IBE Master's, 1. year of study, winter semester, compulsory

  • Programme MPC-AUD Master's

    specialization AUDM-TECH , 2. year of study, winter semester, compulsory-optional

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer


13 hours, compulsory

Teacher / Lecturer