Information and Coding Theory
Explore information measures; compression and channels; error correction. Concepts and connections are available independently of video coverage.
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Source coding represents messages efficiently, with entropy constraining lossless average code length.
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Capacity bounds achievable reliable information rates for a specified channel model.
Hamming distance counts differing positions between equal-length strings.
No codeword is a prefix of another, enabling unambiguous immediate decoding.
Entropy is expected negative log probability for a discrete distribution.
Conditional entropy averages remaining uncertainty after another variable is observed.
Explore information measures; compression and channels; error correction. Concepts and connections are available independently of video coverage.