Information Theory COMP2610

Learning outcomes

More information may be available for enrolled students on the course website on Wattle

More information may be available for enrolled students on the course website at http://cs.anu.edu.au/courses/info/COMP2610

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:

1. Understand and apply fundamental concepts in information theory such as probability, entropy, information content and their inter-relationships.
2. Understand the principles of data compression.
3. Compute entropy and mutual information of random variables.
4. Implement and analyse basic coding and compression algorithms.
5. Understand the relationship of information theoretical principles and Bayesian inference in data modelling and pattern recognition.
6. Understand some key theorems and inequalities that quantify essential limitations on compression, communication and inference.
7. Know the basic concepts regarding communications over noisy channels.