Applied algorithms
Course: Informatics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Applied algorithms
Code
ВК.4.01.02
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
4
Learning outcomes
LO19.1. Know and apply methods of algorithm development, software design and data and knowledge structures.
Form of study
Distance form
Prerequisites and co-requisites
1. Know: basics of the disciplines "Programming", "Discrete Mathematics", "Linear Algebra and Analytical Geometry", "Mathematical Analysis".
2. Be able to: analyze problems, determine estimates of the complexity of algorithms for solving them; apply algorithms in practice and measure their effectiveness.
3. Have methods of building and evaluating algorithms as a means of solving computational problems that arise in various fields of cybernetics.
Course content
The purpose of the discipline "Applied Algorithms" is to get acquainted with the main achievements in some of the most relevant areas of computer science related to coding, retrieval and processing of information.
The discipline "Applied Algorithms" is a component of the educational-professional program "Informatics" training of specialists at the educational qualification level "Bachelor" in the field of knowledge 12 "Information Technology" specialty 122 "Computer Science". It is a discipline that is offered to the student to choose from and is included in the sample list №1. It is taught in the 4th semester of the 2nd year of the bachelor's degree in the amount of 4 ECTS credits.
The course consists of 2 semantic parts. During its study, 2 tests and 2 electronic tests will be performed. The student's work during the semester is evaluated in the form of a test.
Recommended or required reading and other learning resources/tools
1. S. Faro and T. Lecroq: The exact online string matching problem: a review of the most recent results. ACM Computing Surveys (CSUR), 45(2) 2013, p. article 13.
2. Knut D. Iskusstvo programmirovaniia dlia EVM , t.2 – M., 1977, 720s.
3. D. Salomon: Variable-Length Codes for Data Compression, Springer-Verlag, London, U.K., 2007, 196 p.
5. D. Huffman: A method for the construction of minimum-redundancy codes. Proc. IRE, 40 1952, pp. 1098–1101.
6. S. T. Klein and M. Ben-Nissan: On the usefulness of Fibonacci compression codes. Computer Journal, 53(6) 2010, pp. 701–716.
7. A.V. Anisimov, I.O. Zavadskyi. Variable-Length Prefix Codes With Multiple Delimiters // IEEE Transactions on Information Theory, vol. 63, issue 5, p. 2885-2895.–2019.
8. I.O. Zavadskyi. Fast exact pattern matching in a bitstream and 256-ary strings. Proceedings of the Prague Stringology Conference.– 2020, pp.33–47
Planned learning activities and teaching methods
Control work, homework, testing
Assessment methods and criteria
1. Test 1 (written work).
2. Test 2 (written work).
3. Homework (written work).
5. Testing 1 (electronic test).
6. Testing 2 (electronic test).
Language of instruction
Ukrainian language
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline