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Modern Channel Coding, Inference and Learning, 6 credits
Modern kanalkodning, inferens och inlärning, 6 hp
TSKS12
Main field of study
Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Danyo DanevDirector of studies or equivalent
Klas NordbergEducation components
Preliminary scheduled hours: 48 hRecommended self-study hours: 112 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Electrical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Applied Physics and Electrical Engineering, M Sc in Engineering
- Communication Systems, Master's programme
- Computer Science, Master's programme
- Mathematics, Master's programme
- Information Technology, M Sc in Engineering
- Applied Physics and Electrical Engineering - International, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Linear algebra, Probability, Statistics and basic programming skills. Knowledge in algorithms, data structures and communication systems is desirable but not a requirement.
Intended learning outcomes
After completed course the student should be able to:
- define correctly and explain about the following notions: Hamming distance, linear error-correcting 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 K-means clustering algorithms for sets of data points;
Course content
- Introduction to information theory and fundamental limits for communication over noisy channels;
- Modern error-correcting 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;
- K-means 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;
Teaching and working methods
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.
Examination
| LAB1 | Laboratory work | 2 credits | U, G |
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för systemteknikDirector of Studies or equivalent
Klas NordbergExaminer
Danyo DanevCourse website and other links
Education components
Preliminary scheduled hours: 48 hRecommended self-study hours: 112 h
Course literature
Additional literature
Books
- David J.C. MacKay, (2003) Information Theory, Inference and Learning Algorithms
ISBN: 0521642981
Cambridge University Press
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB1 | Laboratory work | 2 credits | U, G |
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Additional literature
Books
David J.C. MacKay, (2003) Information Theory, Inference and Learning Algorithms
ISBN: 0521642981
Cambridge University Press
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
|
X
|
X
|
TEN1
|
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| 1.2 Fundamental engineering knowledge (G1X level) |
|
X
|
X
|
LAB1
TEN1
|
||
| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
X
|
X
|
X
|
LAB1
TEN1
|
||
| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
|
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| 1.5 Insight into current research and development work |
|
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
X
|
LAB1
TEN1
|
||
| 2.2 Experimentation, investigation, and knowledge discovery |
|
X
|
X
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| 2.3 System thinking |
|
X
|
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LAB1
|
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| 2.4 Attitudes, thought, and learning |
|
X
|
X
|
TEN1
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| 2.5 Ethics, equity, and other responsibilities |
|
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
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| 3.2 Communications |
|
|
X
|
LAB1
|
||
| 3.3 Communication in foreign languages |
|
|
X
|
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
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| 4.4 Designing |
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| 4.5 Implementing |
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| 4.6 Operating |
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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