A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
BIK-AG1.21 Algorithms and Graphs 1 Extent of teaching: 14KP+4KC Instructor: Hušek R. Completion: Z,ZK Department: 18101 Credits: 5 Semester: Z Annotation:
The course is presented in Czech. The course covers the basics from the efficient algorithm design, data structures, and graph theory, belonging to the core knowledge of every computing curriculum. Students learn techniques of proofs of correctness of algorithms and techniques of asymptotic mathematics for estimation of their complexity in the best, worse, or average case (the course includes basics from probability theory needed for understanding randomized algorithms). Within exercises students learn applications of studied algorithms for solving practical problems.
Lecture syllabus:
1. Motivation, graph definition, important types of graphs, undirected graphs, graph representation, subgraphs. 2. Connectivity, connected components, DFS, directed graphs, trees. 3. Spanning trees, distances in graphs, BFS, topological ordering. 4. Basic sorting algorithms with the quadratic time complexity. Binary heap as a partial ordered structure, HeapSort. 5. Extendable array, amortized complexity. Binomial Heaps. 6. Operations and properties of binary search trees, balancing strategies, AVL trees. 7. Randomized algorithms. Introduction to probability theory. Hash tables and strategies of collision resolving. 8. Recursive algorithms and Divide and Conquer algorithms. 9. QuickSort. Lower bound of complexity for sorting problem in the comparison model. Special sorting algorithms. 10. Dynamic programming. 11. Minimum spanning trees of edge-labelled graphs. Jarník?s algorithm and Kruskal?s algorithm and their implementations. 12. [2] Shortest paths algorithms on edge-labelled graphs. Seminar syllabus:
1. Implementation of FA. 2. Examples of formal languages. Intuitive considerations of grammars for given languages. Estimation of the classification of a given language in Chomsky hierarchy. 3. Intuitive creation of finite automata (DFA, NFA, with epsilon transitions) for a given langauage. 4. Transformations and compositions of FA. 5. FA with output function and its implementation. 6. Conversions of grammars to FA and vice versa. 7. Considerations, modifications and transformations of regular expressions. 8. Use of regular expressions for text processing tasks (e.g. sh, grep, sed, perl). 9. Creation and implementation of lexical analyzers. 10. Classification of languages. 11. Examples of context-free languages, creation of pushdown automata. 12. Examples of deterministic parsing of context-free languages (e.g. LL, yacc, bison). 13. Examples of context-sensitive and recursively enumerable languages, creation of grammars, creation of Turing machines. Literature:
1. Cormen T.H., Leiserson C.E., Rivest R.L., Stein C. : Introduction to Algorithms (3rd Edition). MIT Press, 2016. ISBN 978-0262033848. 2. Wengrow J. : A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills (2nd Edition). Pragmatic Bookshelf, 2020. ISBN 978-1680507225. 3. Sedgewick R. : Algorithms (4th Edition). Addison-Wesley, 2011. ISBN 978-0321573513. 4. Deo N. : Graph Theory with Applications to Engineering and Computer Science. Dover Publications, 2016. ISBN 978-048680793. 5. Bickle A. : Fundamentals of Graph Theory. AMS, 2020. ISBN 978-1470453428. Requirements:
Entry knowledge: Active algorithmic skills for solving basic types of computational tasks, programming skills in some HLL (Java, C++), and knowledge of basic notions from the mathematical analysis and combinatorics are expected. Students are expected to take concurrent courses BIE-AAG and BIE-ZDM.
Chybí některá textová pole,vyplněny mají být anotace, požadavky, osnova (sylabus), osnova cvičení, studijní materiály, klíčová slova, CZ i EN, webová strana předmětu The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester BIK-IB.21 Information Security 2021 (in Czech) PP 3 BIK-SPOL.21 Unspecified Branch/Specialisation of Study PP 3 BIK-PV.21 Computer Systems and Virtualization 2021 (in Czech) PP 3 BIK-PS.21 Computer Networks and Internet 2021 (in Czech) PP 3 BIK-SI.21 Software Engineering 2021 (in Czech) PP 3
Page updated 18. 4. 2024, semester: L/2020-1, L/2023-4, L/2019-20, Z/2021-2, L/2022-3, Z/2023-4, Z/2019-20, Z/2022-3, L/2021-2, Z/2024-5, Z/2020-1, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška