Academic year 2019/2020
- Course ID
- Prof. Matteo Sereno
- 2nd year
- Teaching period
- Second semester
- D.M. 270 TAF C - Related or integrative
- Course disciplinary sector (SSD)
- INF/01 - informatica
- Formal authority
- Type of examination
- Written and oral
Sommario del corso
The course represents an introduction to classical results of Shannon information theory.
Results of learning outcomes
Knowledge and understanding: upon completion of this course, students should be able to:
- Calculate the information content of a random variable from its probability distribution.
- Relate the joint, conditional, and marginal entropies of variables in terms of their coupled probabilities.
- Define channel capacities and properties using Shannon's Theorems.
- Construct efficient codes for data on imperfect communication channels.
- Introduce the mathematical ideas underlying the theory of error-detection and error-correction using linear codes.
Applying knowledge and understanding: the student will be able to appreciate the mathematical features underlying the digital communication.
Communication skills: the students will be able to explain to a non expert the acquired concepts by using a formallly correct and rigourous exposition, and to discuss with experts about topics coherent with the course contents.
The course is composed of 48 hours of class lectures.
Learning assessment methods
Closed-booked written exam (two hours) - (70%). The written exam will consist of a few questions and/or problems. The mark assigned to each question/problem will depend on the level of difficulty.
Oral test - (30%).
To pass the module students must achieve a pass mark of 60% when all elements are combined.
The course is structured in two parts.
- The first part of the course is devoted to the classical information theory. In particular, the addressed topics are: definition of information and source types, the concept of entropy, source coding, Shannon's first theorem (source coding), uniquely decodable codes, optimality of Huffman coding, models of noisy channels, definition of the channel capacity according to Shannon's theorem (channel coding).
- The second part of the course is devoted to the study of source coding and channel coding algorithms used in many applications, communication systems and networks. As far as channel coding is regarded the course will introduce linear block codes.
Suggested readings and bibliography
- Thomas M. Cover, Joy A. Thomas, "Elements of Information Theory, 2nd Edition", ISBN: 978-0-471-24195-9.
- R. W. Yeung, "Information Theory and Network Coding, ISBN: 978-0-387-79233-0
This course will be delivered at the Computer Science Department
Note: the course web page (on moodle) is at Information theory
Students are invited to register on this web page. It will be the main channel through which all the course information will be distributed.