M8170 Coding
Faculty of ScienceSpring 2017
- Extent and Intensity
- 2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
- Teacher(s)
- prof. RNDr. Jan Paseka, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Jan Paseka, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Mon 20. 2. to Mon 22. 5. Mon 13:00–14:50 M6,01011
- Timetable of Seminar Groups:
- Prerequisites
- Mathematical analysis I. and II., Linear algebra and geometry I. and II., Fundamentals of mathematics, Algebra I, Probability and Statistics.
- 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
- Algebra and Discrete Mathematics (programme PřF, N-MA)
- Applied Informatics (programme FI, B-AP)
- Applied Informatics (programme FI, N-AP)
- Course objectives
- The basic goal of the course is the introduction of the student to establish the mathematical basics of coding theory. Some applications of coding theory are mentioned, specially in the area of data transmission.
At the end of this course, students should be able to:
understand rudiments of coding theory;
explain basic notions and relations among them.
The student will be able to use the acquired knowledge of coding techniques in solving specific problems from the area of data transmission. - Syllabus
- Introduction.
- A very abstract summary. History. Outline of the course.
- Entropy.
- Uncertainty. Entropy and its properties. Information.
- Communication through channels.
- The discrete memoryless channel. Codes and decoding rules. The noisy coding theorem.
- Error-correcting codes.
- The coding problem - need for error correction. Linear codes. Binary Hamming codes. Cyclic codes. Reed--Muller codes.
- General sources.
- The entropy of a general source. Stationary sources. Markov sources.
- The structure of natural languages. English as a mathematical source. The entropy of English.
- Literature
- Roman, Steven, Coding and Information Theory, Graduate Texts in Mathematics, Springer Verlag, 1992
- Adámek, Jiří. Foundations of coding, John Wiley \& Sons, Inc. 1991
- Welsh D., Codes and cryptography, Oxford, University Press, New York, 1988
- Hamming, R. W. Coding and information theory, Prentice-Hall, New-Jersey 1950
- ADÁMEK, Jiří. Kódování. Vyd. 1. Praha: SNTL - Nakladatelství technické literatury, 1989, 191 s. URL info
- Teaching methods
- Lectures: theoretical explanation with practical examples.
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems, homeworks.
Students will be asked to have an active participation at seminars or a written homework that will be lectured at some seminar. The theme will be chosen after the negotiation with the lecturer. - Assessment methods
- Lecture with a seminar. Examination is oral with a written preparation.
The success at the examination is based on providing an exposition with respect to a chosen chapter. - Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught once in two years. - Teacher's information
- http://www.math.muni.cz/~paseka
- Enrolment Statistics (Spring 2017, recent)
- Permalink: https://is.muni.cz/course/sci/spring2017/M8170