Course detail

Data Structures and Algorithms

FEKT-MPC-PDAAcad. year: 2024/2025

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

Language of instruction

Czech

Number of ECTS credits

7

Mode of study

Not applicable.

Guarantor

doc. Ing. Radim Burget, Ph.D.

Department

Department of Telecommunications (UTKO)

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.

Aims

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

 

Lecture

26 hours, 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 isomorphism
L06: Flow and Cuts in Graphs
L07: Neural Networks
L08: Convolutional Neural Networks
L09: Fundamentals of Machine Learning
L10: Recurrent Neural Networks
L11: Reinforcement learning
L12: Neural Networks for trees and graphs
L13: Summary and preparation for final exam 

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

doc. Ing. Radim Burget, Ph.D.

Project

13 hours, compulsory

Teacher / Lecturer

doc. Ing. Radim Burget, Ph.D.