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COMP30690

Academic Year 2024/2025

Information Theory (COMP30690)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Félix Balado Pumarino
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This course provides an introduction to the fundamentals of Information Theory for computer scientists. Shannon's Information Theory is one of the greatest intellectual achievements of the 20th century. It provides a comprehensive view on the concept of information, whereby fundamental limits to the performance of practical algorithms are established. Information Theory concerns all areas where information is processed, stored or transmitted in some way or another. Consequently, it is an essential component in the education of a computer scientist.

About this Module

Learning Outcomes:

- Understand the general relevance of Shannon's Information Theory in the Information Age.
- Review essential probability theory.
- Become acquainted with fundamental information-theoretical concepts such as entropy, mutual information, relative entropy: Jensen's inequality, log-sum inequality, data-processing inequality, sufficient statistics, Fano's inequality.
- Understand the centrality of the asymptotic equipartition property in Information Theory: typical set.
- Understand the fundamentals of data compression: Kraft inequality, optimal codes, Huffman codes, Shannon-Fano-Elias coding.
- Understand the concept of channel capacity: symmetric channels, channel coding theorem, elementary channel coding techniques (repetition, Hamming codes).

Indicative Module Content:

(see above)

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Tutorial

16
Autonomous Student Learning

96
Total

136

Approaches to Teaching and Learning:
Lectures; Reflective learning; Problem-based learning

Requirements, Exclusions and Recommendations
Learning Requirements:

Working knowledge of basic calculus and algebra.

Learning Recommendations:

Knowledge of probability theory would be helpful, although the course is self-contained in this respect.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Assignments Week 6, Week 11 Alternative linear conversion grade scale 40% No

30

 No
Exam (In-person): Final exam End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No

70

 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Yogesh Bansal Tutor
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