IB002 Algorithms and data structures I

Faculty of Informatics
Spring 2022
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)
Bc. Jakub Balabán (seminar tutor)
prof. RNDr. Jiří Barnat, Ph.D. (seminar tutor)
RNDr. Nikola Beneš, Ph.D. (seminar tutor)
Bc. Andrej Čermák (seminar tutor)
Matej Focko (seminar tutor)
Mgr. Jan Horáček (seminar tutor)
Mgr. Jan Koniarik (seminar tutor)
Bc. Nastasia Kovářová (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)
Michal Staník (seminar tutor)
Jakub Šárník (seminar tutor)
Mgr. Mária Švidroňová (seminar tutor)
Bc. Matěj Žáček (seminar tutor)
Mgr. Lukáš Korenčik (assistant)
Bc. Martin Kurečka (assistant)
RNDr. Henrich Lauko (assistant)
Petra Ludvová Hašková, DiS. (assistant)
RNDr. Vladimír Štill, Ph.D. (assistant)
Mgr. Tatiana Zbončáková (assistant)
Mgr. Vojtěch Žák (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
    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 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
Teacher's information
https://is.muni.cz/auth/el/1433/jaro2021/IB002/index.qwarp
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021.
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