Course syllabus

Course-PM

EEN240 Information, inference, and coding lp4 VT26 (7.5 hp)

Course is offered by the Department of Electrical Engineering

Contact details

• Examiner: Christian Häger (christian.haeger@chalmers.se, office room 6439, EDIT building)
• TAs: Bingcheng Chen (chenbin@chalmers.se) and Yuhao Zhang (yuhaozh@chalmers.se)

Course representatives

• tbd

Course purpose

The aim of this course is to provide a foundational understanding of information theory, data compression, and probabilistic inference with applications to modern coding techniques. Students will learn core information-theoretic concepts such as entropy, mutual information, and channel capacity, focusing on efficient data representation and noisy-channel coding. Additionally, the course introduces Bayesian inference and covers modern coding methods for error correction, equipping students with both analytical and practical tools applicable across various technical fields that rely on efficient and reliable information processing.

Schedule

The course schedule can be found in the course memo (see also the course summary below). Room allocations can be found on TimeEdit Links to an external site.

Course literature

The course is based on a set of lecture notes and slides that will be made available in Canvas. For students who wish to dive deeper into the material, the following book is recommended:

David J.C. MacKay, “Information Theory, Inference, and Learning Algorithms”, Cambridge University Press, 2012. The book is freely available for download at www.inference.org.uk/mackay/itila/book.html Links to an external site..

Course memo

The course memo contains a detailed description of the course's learning activities, including the lectures, tutorial sessions, and the course project. 

Learning objectives and syllabus

The learning objectives and syllabus can be found on Studieportalen Links to an external site.. 

Grading

The final course grade will be decided based on the project (maximum score 30), quizzes (maximum score 6), tutorials (maximum score 14), and final exam (maximum score 50). The sum of all scores will decide the grade according to the following table:

• Total score: 0 - 39 => Grade: Fail
• Total score: 40 - 59 => Grade: 3
• Total score: 60 - 79 => Grade: 4
• Total score: >= 80 => Grade: 5

Note that a minimum score of 15 points in the exam is required to pass the course. Furthermore, the project must be passed successfully to pass the course.