UnivIS

Informationstheorie und Codierung

Dozent/in

Details

Zeit/Ort n.V.

  • Do 14:15-15:45, Raum H6 (außer vac) ICS
  • Fr 10:15-11:45, Raum H6 (außer vac) ICS

Inhalt

Introduction to coding and information theory (binomial distribution, (7,4)-Hamming code, parity-check matrix, generator matrix); Probability, entropy, and inference (entropy, conditional probability, Bayes’ law, likelihood, Jensen’s inequality); Inference (inverse probability, statistical inference); Source coding theorem (information content, typical sequences, Chebychev inequality, law of large numbers); Symbol codes (unique decidability, expected codeword length, prefix-free codes, Kraft inequality, Huffman coding); Stream codes (arithmetic coding, Lempel-Ziv coding, Burrows-Wheeler transform); Dependent random variables (mutual information, data processing lemma); Communication over a noisy channel (discrete memory-less channel, channel coding theorem, channel capacity); Noisy-channel coding theorem (jointly-typical sequences, proof of the channel coding theorem, proof of converse, symmetric channels); Gaussian channel (AWGN channel, multivariate Gaussian pdf, capacity of AWGN channel); Binary codes (minimum distance, perfect codes, why perfect codes are bad, why distance isn’t everything); Message passing (distributed counting, path counting, low-cost path, min-sum (=Viterbi) algorithm); Marginalization in graphs (factor graphs, sum-product algorithm); Low-density parity-check codes (density evolution, check node degree, regular vs. irregular codes, girth); Lossy source coding (transform coding and JPEG compression)

Zusätzliche Informationen

www: https://www.studon.fau.de/crs4378948.html