Information theory studies the fundamental limits of the representation and transmission of information. This course provides an introduction to information theory, studying fundamental concepts such as probability, information, and entropy and examining their applications in the areas of data compression, coding, communications, pattern recognition and probabilistic inference.
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Requisite and Incompatibility
You cannot enrol in this course if you have completed COMP2610 or ENGN8534.
Referenced entries (2)
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- COMP2610 · 2027 · saved source not linked
- ENGN8534 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Understand and apply fundamental concepts in information theory such as probability, entropy, information content and their inter-relationships.
Understand the principles of data compression.
Compute entropy and mutual information of random variables.
Implement and analyse basic coding and compression algorithms.
Understand the relationship of information theoretical principles and Bayesian inference in data modelling and pattern recognition.
Understand some key theorems and inequalities that quantify essential limitations on compression, communication and inference.
Know the basic concepts regarding communications over noisy channels.
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | Second Semester | 10382 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |
Complete offering text
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage. The list of offerings for future years is indicative only. Class summaries, if available, can be accessed by clicking on the View link for the relevant class number. 2027 Second Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 10382 26 Jul 2027 02 Aug 2027 31 Aug 2027 29 Oct 2027 In Person N/A
Source evidence (1 saved copy)
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