Duration
30h Th, 15h Pr, 30h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
NB: This course is given in English, since the 2013-2014 academic year.
Information theory provides a quantitative measure of the information provided by a message or an observation. This notion was introduced by Claude Shannon in 1948 in order to establish the limits of what is possible in terms of data compression and transmission over noisy channels. Since these times, this theory has found many applications in telecommunications, computer science ans statistics. The course is composed of three parts.
- The foundations of information theory.
- An introduction to coding theory for data compression, error-free communication, and cryptography.
- An overview of other applications of information theory.
Learning outcomes of the learning unit
Successful completion of this course means that the student has acquired a good understanding of the principles of information theory and will be able to exploit these principles in order to analyze and design source and channel coding algorithms.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, III.4, IV.1, IV.2, VI.1, VI.2, VI.3, VII.1, VII.2, VII.4 of the MSc in biomedical engineering.
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, II.3, III.1, III.2, III.3, III.4, IV.1, IV.2, VI.1, VI.2, VI.3, VII.1, VII.2, VII.4 of the MSc in data science and engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, III.4, IV.1, IV.2, IV.8, VI.1, VI.2, VI.3, VII.1, VII.2, VII.4 of the MSc in electrical engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, III.4, IV.1, IV.2, VI.1, VI.2, VI.3, VII.1, VII.2, VII.4 of the MSc in computer science and engineering.
Prerequisite knowledge and skills
Probability calculus and elements of statistics.
Planned learning activities and teaching methods
Exercise sessions and homework.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
2nd semester
Recommended or required readings
See course Web page: https://people.montefiore.uliege.be/lwh/Info/
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Written work / report
Additional information:
2 practical project works by groups of two students.
Written exam on the theory and exercises (June and/or August).