Course detail
Data Structures and Algorithms
FEKT-MPA-TINAcad. year: 2024/2025
1. Information representation – object oriented design
2. Information representation – introduction to data structures, abstract data types
3. Computability and complexity, deterministic and non-deterministic automata
4. Representation of information - linear data structures
5. Representation of information - tree data structures
6. Representation of information - graphs
7. Access Information– spanning tree
8. Access Information - finding a path in graphs
9. Access Information - mining knowledge from data
10. Information Disclosure - Optimization
11. Information Disclosure - Status Space Search, Genetic Algorithms
12. Processes, threads, and parallel calculations
13. Parallel, sequential and random algorithms. Distributed algorithms
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
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
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.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
- Programme MPA-CAN Master's 1 year of study, winter semester, compulsory
- Programme MPA-TEC Master's 1 year of study, winter semester, compulsory-optional
- Programme MPAD-CAN Master's 1 year of study, winter semester, compulsory
- Programme MPAJ-TEC Master's 1 year of study, winter semester, compulsory-optional
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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