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EQ2845 Information Theory and Source Coding 7,5 hp

Course memo Spring 2022-60225

Version 1 – 01/20/2022, 4:33:32 PM

Course offering

Spring 2022-1 (Start date 18/01/2022, English)

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Course memo Spring 2022

Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019

Content and learning outcomes

Course contents

This course introduces the principles of information theory and source coding, discusses fundamental source coding concepts, and provides hands-on experience for selected popular source coding algorithms. The course includes topics on information and entropy, lossless coding, Shannon's noiseless source coding theorem, lossy coding, rate distortion, Shannon's noisy source coding theorem, scalar and vector quantization, transform and predictive coding.

Intended learning outcomes

After passing this course, participants should be able to

- describe and use the principles of information theory, like entropy, mutual information, asymptotic equipartition, data processing, prefix codes, Kraft inequality, noiseless source coding, maximum entropy, rate distortion, noisy source coding, Shannon lower bound, backward channel, reverse waterfilling, energy concentration, etc. to develop source coding algorithms,

- develop source coding schemes for lossless coding, like Huffman coding, arithmetic coding, Lempel-Ziv coding, universal source coding,

- develop source coding schemes for lossy coding, like scalar and vector quantization, Lloyd-Max quantization, entropy-constrained quantization, high-rate quantization, transform coding, predictive coding,

- implement (for example with MatLab) and assess the developed source coding schemes / algorithms, 

- explain coding design choices using the principles of information theory,

- develop source coding schemes for a given source coding problem,

- model and assess source coding schemes using the principles of information theory,

- analyze given source coding problems, identify and explain the challenges, propose possible solutions, and explain the chosen design.

To achive higher grades, participants should also be able to

- solve more advanced problems in all areas mentioned above.

Learning activities

Lectures on selected topics with discussions, exercises with problem solving and peer discussions, individual homework assignements with problem solving and software simulations.

Preparations before course start

Literature

No information inserted

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • INL1 - Assignment, 1.5 credits, Grading scale: P, F
  • TEN1 - Exam, 6.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

 Homework assignments 1.5 ECTS (P/F). Written exam 6 ECTS (A-F).

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

No information inserted

Round Facts

Start date

Missing mandatory information

Course offering

  • Spring 2022-60225

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Contacts

Course Coordinator

Teachers

Teacher Assistants

Examiner