After successful completion of the course, students are able to

• define and compute maximal und mean uncertainty
• cite and apply Kraft's inequality
• define and compute entropy, relative entropy, conditional entropy, information
• state properties of entropy
• define information sources
• cite and apply the Shannon-McMillan-Breiman theorem
• define codes
• define and check decipherabilty of codes
• state theoretical limits of data compression
• define and construct Huffman-, Shannon-, Fano- and arithmetic codes
• define channels and their capacity
• define Hamming distance
• define minimal distance of a code, error detecting and error correcting codes
• define and construct linear codes

uncertainty, Kraft's inequality, entropy, information, information sources, Shannon-McMillan-Breiman theorem, codes, decipherabilty, data compression, Huffman-, Shannon-, Fano- and arithmetic codes, channels, Hamming distance, minimal distance of a code, error detecting and error correcting codes, linear codes

Lecture

oral exam