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.
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of the course, the student will have background knowledge necessary to understand problems in data compression, storing and communication and undertake advanced courses on statistical inference, machine learning and information engineering. In particular, the student will be able 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.
Assignment 1 (10%) Assignment 2 (20%) Assignment 3 (20%) Final Exam (50%)
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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WorkloadTwenty-six one-hour lectures and five two-hour tutorial sessions.
Requisite and Incompatibility
Information Theory, Inference, and Learning Algorithms by David MacKay, Cambridge University Press, 2003.
Additional reading: Elements of Information Theory by Cover and Thomas, 2nd Edition, New York, Wiley, 2006.
Some background in elementary statistics and probability.
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
Offerings, Dates and Class Summary Links
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|5442||26 Jul 2021||02 Aug 2021||14 Sep 2021||29 Oct 2021||In Person||N/A|