This course presents the principles and techniques fundamental to the representation, processing and transmission of information. It equips students with the knowledge and tools to answers questions such as: What is information and how to quantify it? How much can we compress data without distortion? How to reliably store and transmit information in a way that is robust to data corruption? And how to learn from data with uncertainty? The answers to these questions often find their applications in electronic and computer engineering fields and beyond, such as electronic systems, control systems, telecommunication systems, computer systems, machine learning, data science, neuroscience, bioinformatics and linguistics.
The course begins by introducing the basic concepts of probability and random variables, leading to probabilistic inference. It then continues with the following topics: concepts and tools of information theory, data compression principles and algorithms, error detection and correction codes for data storage and transmission, digital modulation and communication channel characteristics.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Apply probabilistic modelling to characterise physical phenomena involving randomness and interdependency and conduct Bayesian inference.
- Use concepts and tools from information theory in data modelling and processing.
- Understand the fundamental limits in data compression and select suitable compression algorithms according to design criteria.
- Apply and evaluate error detection and error correction methods for protecting information in storage and transmission systems.
- Evaluate and compare digital modulation methods and construct appropriate designs based on the communication channel characteristics and performance.
- Design and assess the performance of information engineering processes through computer simulation.
- Apply engineering design practices in laboratory and project settings individually and in a small team and communicate the results effectively.
Indicative Assessment
- Assignments (20) [LO 1,2,3,4,5]
- Labs (15) [LO 1,3,4,5,6,7]
- Project (15) [LO 1,5,6,7]
- Final Exam (50) [LO 1,2,3,4,5]
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
Approximately 10 hours per week is expected with a total of 130 hours for the entire course. The workload covers the learning activities of lecture content, tutorials, computer labs and project, as well as adequate self-study and completion of assessments.
Requisite and Incompatibility
Prescribed Texts
None.
Preliminary Reading
D. J. C. MacKay, "Information Theory, Inference, and Learning Algorithms'', Cambridge University Press, 2003.
T. M. Cover and J. A. Thomas, "Elements of Information Theory", 2nd Edition, Wiley, 2006.
J. G. Proakis and M. Salehi, "Communications Systems Engineering", 2nd Edition, Pearson, 2002.
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 |
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.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 5132 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |