Course detail

Theoretical Informatics

FEKT-GTINAcad. year: 2019/2020

Object oriented design. Abstract datat types, theoretical models, directed and undirected graphs, graph representation methods. Deterministic and nondeterministic automata. Data structures and objects. Spanning tree, shortest paths in graphs, Parallel and sequential algorithms. Distributed algorithms. Optimization, genetic algorithms.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Radim Burget, Ph.D.

Department

Department of Telecommunications (UTKO)

Learning outcomes of the course unit

Students have skills of design and implementation of various forms of abstract data types and its application to solve specific problems. To solve them the stduents can use linear, tree and graph data structures, furthemore they can search in the data structures and used genetic algorithms for search in a search space and optimization.

Prerequisites

The subject knowledge on the Bachelor degree level is required.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Techning 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

Course curriculum

Lectures:
1. Information representation, objective oriented design.
2. Information representation, introduction to data structures.
3. Complexity, computability and automata theory.
4. Information representation, linear data structures and sorting.
5. Information representation - tree data structures.
6. Information representation - graph theory.
7. Information acccess - spanning tree.
8. Information acccess - graph search.
9. Information acccess - data mining.
10. Information acccess - decision trees.
11. Information acccess - genetic algorithms.
12. Information acccess - genetic programming.
13. Multithreaded computations, parallelization.

Computer excercises:
1. Introduction to OON.
2. Information representation I.
3. Information representation II.
4. Linear data structures.
5. Binary search trees.
6. Graphs theory.
7. Search in Graphs.
8. Midexam.
9. Search in Graphs - Dijkstra algorithm.
10. Data mining - decision trees.
11. Optimization - genetic algorithms.

Work placements

Not applicable.

Aims

To provide theoretical knowledge of information gathering, processing and sharing in communication systems, and of their structure, behaviour and mutual interaction.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

LEUWEN, J., WATANABE, O., HAGIYA, M. Exploring New Frontiers of Theoretical Informatics. Springer, 2000. (EN)
GOODRICH, T.M., TAMASSIA, R. Data Structures and Algorithms in Java. 2000. (EN)
BATTISTA, G., TOLLIS, I. Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, 1998. (EN)

Recommended reading

Not applicable.

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme TECO-G Master's

    branch G-TEC , 1. year of study, winter semester, optional specialized
    branch G-TEC , 2. year of study, winter semester, optional specialized

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. Radim Burget, Ph.D.

Syllabus

1. Information representation, objective oriented design.
2. Information representation, introduction to data structures.
3. Complexity, computability and automata theory.
4. Information representation, linear data structures and sorting.
5. Information representation - tree data structures.
6. Information representation - graph theory.
7. Information acccess - spanning tree.
8. Information acccess - graph search.
9. Information acccess - data mining.
10. Information acccess - decision trees.
11. Information acccess - genetic algorithms.
12. Information acccess - genetic programming.
13. Multithreaded computations, parallelization.

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

doc. Ing. Radim Burget, Ph.D.

Syllabus

1. Introduction to OON.
2. Information representation I.
3. Information representation II.
4. Linear data structures.
5. Binary search trees.
6. Graphs theory.
7. Search in Graphs.
8. Midexam.
9. Search in Graphs - Dijkstra algorithm.
10. Data mining - decision trees.
11. Optimization - genetic algorithms.

eLearning

eLearning: currently opened course