PhD Course - Information Theory
DTU Department of Photonics Engineering
To allow Ph.D. students to use methods from information theory in their research e.g. in communication systems.
Learning objectives:
A student who has met the objectives of the course will be able to:
- Calculate the entropy of memoryless sources and Markov sources
- Calculate the mutual information, divergence, cross entropry and channel capacity for correlated signals, e.g. simple channels
- Apply Huffman coding and other simple source coding methods
- Determine error-correcting capability of linear codes and bounds for their performance
- Find parameters of some well-known codes, e.g. Hamming, Reed-Solomon and product codes
- Calculate and analyze adaptive code lengths in source coding
- Explain basic elements in two-dimensional information theory
- Apply methods from information theory to problems related to their ph.d. project
Contents:
The course covers fundamental results in information theory: Entropies for various classes of sources including 2-D, cross-entropy, divergence, channel capacity and coding theorems for information channels, coding theorems for data compression. Some recent results will be selected depending on the interests of the participants.