• Offered by Research School of Computer Science
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Computer Science
  • Academic career UGRD
  • Course convener
    • Dr Robert Williamson
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in Second Semester 2016
    See Future Offerings

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%)

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Workload

Twenty-six one-hour lectures and five two-hour tutorial sessions.

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.

Assumed Knowledge

Some background in elementary statistics and probability.

Majors

Fees

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:
2
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.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2016 $3276
International fee paying students
Year Fee
2016 $4368
Note: Please note that fee information is for current year only.

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.

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.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
8210 18 Jul 2016 29 Jul 2016 31 Aug 2016 28 Oct 2016 In Person N/A

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