#
FI:IB002 Algorithms I - Course Information

## IB002 Algorithms and data structures I

**Faculty of Informatics**

Spring 2023

**Extent and Intensity**- 2/2/1. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Taught in person. **Teacher(s)**- prof. RNDr. Ivana Černá, CSc. (lecturer)

prof. RNDr. Jiří Barnat, Ph.D. (seminar tutor)

RNDr. Nikola Beneš, Ph.D. (seminar tutor)

Matej Focko (seminar tutor)

Mgr. Tomáš Foltýnek, Ph.D. (seminar tutor)

Filip Kučerák (seminar tutor)

doc. Mgr. Jan Obdržálek, PhD. (seminar tutor)

Bc. Matěj Pavlík (seminar tutor)

RNDr. Jaromír Plhák, Ph.D. (seminar tutor)

doc. RNDr. Vojtěch Řehák, Ph.D. (seminar tutor)

Bc. Jakub Šárník (seminar tutor)

Dávid Šutor (seminar tutor)

Bc. Matěj Žáček (seminar tutor)

Bc. Jakub Balabán (assistant)

Bc. Marek Jankola (assistant)

Mgr. Lukáš Korenčik (assistant)

Bc. Nastasia Kovářová (assistant)

Petra Ludvová Hašková, DiS. (assistant)

Mgr. Kristýna Pekárková (assistant)

RNDr. Vladimír Štill, Ph.D. (assistant)

Mgr. Tatiana Zbončáková (assistant) **Guaranteed by**- prof. RNDr. Ivana Černá, CSc.

Department of Computer Science - Faculty of Informatics

Supplier department: Department of Computer Science - Faculty of Informatics **Prerequisites**-
**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**- there are 58 fields of study the course is directly associated with, display
**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 basic 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 the correctness of simple iterative and recursive algorithms,

- implement algorithms in the selected programming language (Python). **Syllabus**- Basic analysis of algorithms: The 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 the time complexity of sorting.
- Graphs and their representation. Graph search. Depth-first traversal, topological sort, strongly connected components. Breadth-first traversal, bipartite graphs. Shortest paths, algorithm Bellman-Ford, Dijkstra's algorithm.

**Literature**- CORMEN, Thomas H.
*Introduction to algorithms*. 3rd ed. Cambridge, Mass.: MIT Press, 2009. xix, 1292. ISBN 9780262533058. info

*required literature*- SKIENA, Steven S.
*The algorithm design manual*. New York: Springer, 1998. xvi, 486. ISBN 0387948600. info

*recommended literature*- CORMEN, Thomas H.
**Teaching methods**- The course is organized as a series of lectures accompanied by 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/jaro2021/IB002/index.qwarp
**Language of instruction**- Czech
**Follow-Up Courses****Further Comments**- The course is taught annually.

The course is taught: every week. **Listed among pre-requisites of other courses****IB114**Introduction to Programming and Algorithms II

(IB111 || IB113) && !IB002 && !NOW(IB002)**IV003**Algorithms and Data Structures II

(IB002 || program(PřF:N-MA)) && !IB108**IV100**Parallel and distributed computations

IB002**PB006**Principles of Programming Languages and OOP

(IB111 || IB002) && PB071

**Teacher's information**- https://is.muni.cz/auth/el/1433/jaro2021/IB002/index.qwarp

- Enrolment Statistics (Spring 2023, recent)
- Permalink: https://is.muni.cz/course/fi/spring2023/IB002