FI:IB002 Algorithms I - Course Information
IB002 Algorithms and data structures IFaculty of Informatics
- Extent and Intensity
- 2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- prof. RNDr. Ivana Černá, CSc. (lecturer)
RNDr. Nikola Beneš, Ph.D. (seminar tutor)
doc. RNDr. Tomáš Brázdil, Ph.D. (seminar tutor)
Mgr. Tomáš Effenberger (seminar tutor)
Bc. Jan Horáček (seminar tutor)
Mgr. Jan Koniarik (seminar tutor)
RNDr. Henrich Lauko (seminar tutor)
Bc. Adam Matoušek (seminar tutor)
Bc. Mária Michalíková (seminar tutor)
RNDr. Petr Novotný, Ph.D. (seminar tutor)
doc. Mgr. Jan Obdržálek, PhD. (seminar tutor)
Mgr. Juraj Pančík (seminar tutor)
RNDr. Jaromír Plhák, Ph.D. (seminar tutor)
doc. RNDr. Vojtěch Řehák, Ph.D. (seminar tutor)
RNDr. Bc. Dominik Velan (seminar tutor)
Mgr. Viktória Vozárová (seminar tutor)
Bc. Tatiana Zbončáková (seminar tutor)
Mgr. Lukáš Korenčik (assistant)
RNDr. Henrich Lauko (assistant)
Bc. Miloslav Staněk (assistant)
RNDr. Vladimír Štill (assistant)
- Guaranteed by
- prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science - Faculty of Informatics
Supplier department: Department of Computer Science - Faculty of Informatics
- Thu 21. 2. to Thu 9. 5. Thu 14:00–15:50 D3, Thu 14:00–15:50 D1
- Timetable of Seminar Groups:
IB002/konzultace02: Mon 17:00–18:40 A417, T. Zbončáková, Bude i 17. a 24. 4. a 15. 5. od 9 do 11 v A417.
IB002/konzultace03: Tue 13:00–14:40 A417, A. Matoušek, Nepřihlašuje se, není cvičení, ale dobrovolná konzultace.
IB002/01: Mon 18. 2. to Mon 13. 5. Mon 8:00–9:50 A319, V. Řehák
IB002/02: Mon 18. 2. to Mon 13. 5. Mon 12:00–13:50 A218, P. Novotný
IB002/03: Mon 18. 2. to Mon 13. 5. Mon 14:00–15:50 A318, N. Beneš
IB002/04: Mon 18. 2. to Mon 13. 5. Mon 16:00–17:50 A320, J. Obdržálek
IB002/05: Mon 18. 2. to Tue 14. 5. Tue 8:00–9:50 A218, J. Obdržálek
IB002/06: Tue 19. 2. to Tue 14. 5. Tue 8:00–9:50 C511, J. Horáček
IB002/07: Tue 19. 2. to Tue 14. 5. Tue 10:00–11:50 A218, J. Obdržálek
IB002/08: Tue 19. 2. to Tue 14. 5. Tue 10:00–11:50 A318, T. Zbončáková
IB002/09: Tue 19. 2. to Tue 14. 5. Tue 12:00–13:50 A217, V. Vozárová
IB002/10: Tue 19. 2. to Tue 14. 5. Tue 12:00–13:50 A218, J. Pančík
IB002/11: Tue 19. 2. to Tue 14. 5. Tue 14:00–15:50 A217, V. Vozárová
IB002/12: Tue 19. 2. to Tue 14. 5. Tue 14:00–15:50 A319, T. Zbončáková
IB002/13: Tue 19. 2. to Tue 14. 5. Tue 18:00–19:50 A319, J. Pančík
IB002/14: Wed 20. 2. to Wed 15. 5. Wed 8:00–9:50 A218, J. Plhák
IB002/15: Wed 20. 2. to Wed 15. 5. Wed 8:00–9:50 B410, P. Novotný
IB002/16: Wed 20. 2. to Wed 15. 5. Wed 10:00–11:50 A318, P. Novotný
IB002/17: Wed 20. 2. to Wed 15. 5. Wed 12:00–13:50 A218, J. Plhák
IB002/18: Wed 20. 2. to Wed 15. 5. Wed 14:00–15:50 A218, V. Řehák
IB002/19: Wed 20. 2. to Wed 15. 5. Wed 16:00–17:50 A319, T. Effenberger
IB002/20: Thu 21. 2. to Thu 16. 5. Thu 8:00–9:50 B411, D. Velan
IB002/21: Thu 21. 2. to Thu 16. 5. Thu 16:00–17:50 A320, J. Koniarik
IB002/22: Fri 22. 2. to Fri 17. 5. Fri 8:00–9:50 A320, D. Velan
IB002/23: Fri 22. 2. to Fri 17. 5. Fri 10:00–11:50 B410, T. Brázdil
IB002/24: Fri 22. 2. to Fri 17. 5. Fri 12:00–13:50 A218, M. Michalíková
- IB001 Intro to Prog. using C || IB111 Foundations of Programming || IB999 Programming Test
The students should comprehend the basic notions on the level of IB111 Introduction to Programming and IB000 Mathematical Foundations of Computer Science Students should be able to: understand and apply basic constructs of programming languages (e.g., conditions, loops, functions, basic data types) in Python, know principles of recursion, and several basic algorithms. Students should know the basic mathematical notions; understand the logical structure of mathematical statements and mathematical proofs, specially mathematical induction; know discrete mathematical structures such as finite sets, relations, functions, and graph including their applications in informatics.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, B-AP)
- Bioinformatics (programme FI, B-AP)
- Economic Information Systems (programme ESF, B-SI)
- Informatics with another discipline (programme FI, B-EB)
- Informatics with another discipline (programme FI, B-FY)
- Informatics with another discipline (programme FI, B-IO)
- Informatics with another discipline (programme FI, B-MA)
- Informatics with another discipline (programme FI, B-TV)
- Public Administration Informatics (programme FI, B-AP)
- Mathematical Informatics (programme FI, B-IN)
- Modelling and Calculations (programme PřF, B-MA)
- Mathematics (programme PřF, B-MA)
- Parallel and Distributed Systems (programme FI, B-IN)
- Computer Graphics and Image Processing (programme FI, B-IN)
- Computer Networks and Communication (programme FI, B-IN)
- Computer Systems and Data Processing (programme FI, B-IN)
- Programmable Technical Structures (programme FI, B-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
- Course objectives
- The course presents basic techniques of the analysis of algorithms, data structures, and operations. Students should correctly apply the basic data structures and algorithms as well as apply the algorithm design and analysis techniques when designing new algorithms. Students implement their algorithms in programming language Python.
- Learning outcomes
- After enrolling the course students are able to:
- actively use and modify basis sorting algorithms and graph algorithms,
- actively used basic techniques for designing algorithms (divide et impera, recursion) and design simple algorithms,
- actively used and modify basic static and dynamic data structures,
- employ time complexity and correctness of algorithms,
- analyze time complexity and prove correctness of simple iterative and recursive algorithms,
- implement algorithms in the selected programming language (Python).
- Basic analysis of algorithms: Correctness of algorithms, input and output conditions, partial correctness, convergence, verification.
- Length of computation, algorithm complexity, problem complexity. Asymptotical analysis of time and space complexity, growth of functions.
- Algorithm design techniques. Divide et impera and recursive algorithms.
- Fundamental data structures: lists, queues. Representation of sets, hash tables. Binary heaps. Binary search trees, balanced trees (B trees, Red-black trees).
- Sorting algorithms: quicksort, mergesort, heapsort, lower bound for time complexity of sorting.
- Graphs and their representation. Graph search. Depth-first traversal, topological sort, strongly connected components. Breath-first traversal, bipartite graphs. Shortest paths, algorithm Bellman - Ford, Dijkstra's algorithm.
- required literature
- CORMEN, Thomas H. Introduction to algorithms. 3rd ed. Cambridge, Mass.: MIT Press, 2009. xix, 1292. ISBN 9780262533058. info
- recommended literature
- SKIENA, Steven S. The algorithm design manual. New York: Springer, 1998. xvi, 486. ISBN 0387948600. info
- Teaching methods
- The course is organized as a series of lectures accompanied with exercises.
- Assessment methods
- The evaluation consists of written final exam and written exams during the term. Details can be found in learning materials https://is.muni.cz/auth/el/1433/jaro2019/IB002/index.qwarp
- Language of instruction
- Follow-Up Courses
- Further Comments
- Study Materials
The course is taught annually.
- Listed among pre-requisites of other courses
- Teacher's information