Course has been adjusted for remote participation in 2021. Some on-campus activities are available. Attendance at these where possible is encouraged.
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.
Learning Outcomes
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.
Indicative Assessment
Assignment 1 (10%) Assignment 2 (20%) Assignment 3 (20%) Final Exam (50%)The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Twenty-six one-hour lectures and five two-hour tutorial sessions.Requisite and Incompatibility
Prescribed Texts
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.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you 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). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2021 | $4410 |
- International fee paying students
| Year | Fee |
|---|---|
| 2021 | $5880 |
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.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 5599 | 26 Jul 2021 | 02 Aug 2021 | 14 Sep 2021 | 29 Oct 2021 | In Person | N/A |
