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 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.

• 6 – 20

Lectures, seminars, presentations by participants.