TSKS12 
Modern Channel Coding, Inference and Learning, 6 ECTS credits.
/Modern kanalkodning, inferens och inlärning/
For:
CS
D
DAV
I
Ii
IT
MMAT
SY
U
Y


Prel. scheduled
hours: 48
Rec. selfstudy hours: 112


Area of Education: Technology
Main field of studies: Electrical Engineering


Advancement level
(G1, G2, A): A


Aim:
After completed course the student should be able to:
 define correctly and explain about the following notions: Hamming distance, linear errorcorrecting code, LDPC code, “Turbo” code, optimal decoding, iterative decoding, decoding region, channel capacity, density evolution, Monte Carlo simulations, marginalization, neural network;
 passably implement decoding algorithms for modern channel codes as well as plot and analyze performance of those;
 fairly well handle necessary mathematical tools: random variables variables, Bayesian inference, Monte Carlo methods, neural networks;
 independently use advanced channel coding techniques in practical applications;
 implement Kmeans clustering algorithms for sets of data points


Prerequisites: (valid for students admitted to programmes within which the course is offered)
Linear algebra, Probability theory, Statistics and basic programming skills. Knowledge in algorithms, data structures and communication systems is desirable but not a requirement.
Note: Admission requirements for nonprogramme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.


Organisation:
Teaching is organized in lectures, exercises and laboratory work. The laboratory work consists of programming tasks connected to the theory presented during the lectures. The programming can be carried out in R, C++, Python, Matlab or similar programming language.


Course contents:
 Introduction to information theory and fundamental limits for communication over noisy channels;
 Modern errorcorrecting codes: LDPC codes and "Turbo" codes;
 Optimal decoding: ML och MAP decoding;
 Iterative decoding algorithms and analysis av their performance;
 Bayesian inference and examples of its applications;
 Kmeans clustering algorithms;
 Exact marginalization;
 Monte Carlo methods for simulation of physical systems;
 Introduction to neural networks: single neurons and examples;
Capacity of a single neuron;


Course literature:
David J. C. MacKay, Information Theory, Inference & Learning Algorithms, Cambridge University Press, 2003; ISBN:0521642981


Examination: 

Written examination Laboratory work 
4 ECTS 2 ECTS




Course language is English.
Department offering the course: ISY.
Director of Studies: Klas Nordberg
Examiner: Danyo Danev

