View Specification DocumentElements of Information Theory (UESTC) UESTCHN3010
- Academic Session: 2026-27
- School: School of Engineering
- Credits: 8
- Level: Level 3 (SCQF level 9)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
Information theory is the foundation theory of modern digital communication systems and it have also been extended to many applications including natural language processing, biology, economics, statistics and data analysis. This course is a basic course for majors in Communications Engineering, and Electronic Engineering. After completion of the course, students will have a preliminary understanding of the basic concepts, theories, methods, and applications of information theory, including but not limited to source entropy, lossless source coding, capacity of discrete and continuous channel, basic channel coding methods, and rate distortion functions.
Timetable
Course will be delivered continuously in the traditional manner at UESTCHN.
Requirements of Entry
Mandatory Entry Requirements
None
Recommended Entry Requirements
None
Excluded Courses
None
Co-requisites
None
Assessment
Assessment
Total ＝ Reports（45%）+ Presentation（5%）+ Final Examination（50%）
The reports are produced from practical work completed by students in the field of information theory as stated in the course handbook.
Main Assessment In: December
Course Aims
This course introduces the fundamentals of information theory, the basic limitations of information compression and transmission in communication systems, and the analytical methods to apply the concepts of information theory for the analysis of related disciplines in information science.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Apply the concept of entropy to solve complex problems for both discrete and continuous source in communications systems, such as discrete memoryless and Markov source, continuous source.
■ Evaluate the notion of average mutual information and channel capacity, apply the Shannon capacity formula to analyse complex problems in typical discrete or continuous channel and to have a deeper understanding of basic limitations in typical communication systems,
■ Evaluate the characteristics of lossless source coding and channel coding including linear block codes, and apply both source and channel coding techniques to recognise the limitations of information compression and communications systems, respectively.
■ Evaluate lossy compression by the rate distortion theory for single/multiple-symbol information source with Shannon's Third theorem, and apply it to discuss its application for complex communications engineering problems.