PhD Course – Information Theory

• 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

The course covers the fundamental results in information theory:
Entropies for various classes of sources including 2-D, cross-entroi, 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.

Lectures, seminars, presentations by participants.