Course detail
Data Structures and Algorithms
FEKT-MPA-TINAcad. year: 2021/2022
Information representation, computability and complexity, deterministic and non-deterministic automata, linear data structures, tree data structures, graphs, knowledge mining, optimization, processes, threads and parallel computations, distributed algorithms
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
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
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
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