Course detail

Data Structures and Algorithms

FEKT-MPC-PDAAcad. year: 2021/2022

Complexity theory, genetic algorithms, genetic programming, graph theory, graph equivalence, inforamtion representation, neural networks, reinforcement learning, embeddings.

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

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)

Planned learning activities and teaching methods

Teachning methods include lectures, computer laboratories and practical laboratories. Course is taking advantage of e-learning (Moodle) system. Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

final examination

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. Information representation, introduction
2. Complexity theory, selected examples of complexity
3. Graph theory, analysis, factorization
4. Theory of graphs, groups, availability, bipartite
5. Graphs equivalence
6. Information representation - machine learning
7. Information representation - network types
8. Information representation - linear regression
9. Information representation - logistic regression, classification
10. Information representation - feed forward neural network
11, Information representation - recurrent neural network
12. Information representation - reinforcement learning
13. Information representation - NN with graphs and trees

 

Aims

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

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme MPC-IBE Master's, 1. year of study, winter semester, 7 credits, compulsory

  • Programme MPC-AUD Master's

    specialization AUDM-TECH , 2. year of study, winter semester, 7 credits, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

Project

13 hours, compulsory

Teacher / Lecturer

eLearning