Course syllabus adopted 2025-02-05 by Head of Programme (or corresponding).
Overview
- Swedish nameInformation, inferens och kodning
- CodeEEN240
- Credits7.5 Credits
- OwnerMPICT
- Education cycleSecond-cycle
- Main field of studyElectrical Engineering
- DepartmentELECTRICAL ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 90131
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0125 Examination 7.5 c Grading: TH | 7.5 c |
In programmes
Examiner
Christian Häger- Assistant Professor, Communication, Antennas and Optical Networks, Electrical Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
A solid foundation in probability theory, linear algebra, and calculus is required.Aim
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.Learning outcomes (after completion of the course the student should be able to)
- Define entropy and mutual information and explain their operational meaning in the context of data compression and noisy-channel coding
- Describe Shannons source coding and channel coding theorems
- Apply principles of data compression by implementing lossless coding methods, such as Huffman coding, and assess their efficiency for discrete memoryless sources
- Perform probabilistic inference in data-driven contexts by applying Bayesian reasoning to make optimal decisions under uncertainty, with practical applications in graphical models and trellis structures
- Evaluate and implement modern coding techniques, including block codes, convolutional codes, and low-density parity-check (LDPC) codes, for enhanced data reliability
- Critically assess different coding and decoding methods and their impact on error performance and computational efficiency
- Apply message-passing algorithms for inference in graphical models and understand the iterative processes that underlie advanced decoding techniques in modern coding theory
Content
- Introduction to information theory: entropy, conditional entropy, mutual information, and their operational meanings
- Data compression and efficient representation: Shannons source coding theorem, symbol codes, Krafts inequality, Huffman coding, block coding
- Probabilities and inference: introduction to Bayesian reasoning, optimal decisions under uncertainty, inference in graphical models and trellises
- Noisy-channel coding: Shannons channel capacity theorem; block and convolutional codes, sparse graph codes including low-density parity-check (LDPC) codes, iterative message-passing algorithms
Organisation
The course is comprised of approximately 18 lectures, 11 exercise sessions, 3 quizzes, and 1 project.
Literature
Stefan M. Moser and Po-Ning Chen, A Student's Guide to Coding and Information Theory, Cambridge University Press, 2012. The book is available at Cremona.Examination including compulsory elements
The final grade (TH) is based on scores from a project, quizzes, and a written exam. The project is mandatory in the sense that it must be passed to pass the course.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.