Information Theory |
|
|---|---|
| Professors | Michael Filippakis |
| Course category | Core |
| Course ID | DS-805 |
| Credits | 5 |
| Lecture hours | 3 hours |
| Lab hours | 2 hours |
| Digital resources | View on Aristarchus (Open e-Class) |
Learning Outcomes
The aim of this course is to support the students in learning the principles, concepts and applications of Information Theory. Information theory is a discipline in applied mathematics involving the quantification of data with the goal of enabling as much data as possible to be reliably stored on a medium or communicated over a channel. The measure of information, known as information entropy, is usually expressed by the average number of bits needed for storage or communication.
Course Contents
- Concepts of entropy and information; channel capacity; channel coding; the Shannon’s theorem; error correction codes and decoding methods.
- Basic definitions of probabilities.
- Source coding.
- Channel capacity.
- Channel coding.
- The Shannon’s theorem.
- Error connection codes and decoding methods.
Recommended Readings
- Teaching Notes.
- Thomas M. Cover & Joy A. Thomas (2006): Elements of Information Theory, Second Edition, Wiley, ISBN: 0-471-24195-4.
- MacKay D.J.C. (2003): Information Theory, Inference, and Learning Algorithms, Cambridge University Press.