Information Theory and Coding (UCB FA24 EE229A, rewritten in 2025)

INFOTH Note 23: Parallel Gaussian Channels, WSS, Distributed Source Compression

Parallel Gaussian Channel Model, Water-Filling Theorem, Szegő Theorem for Colored Noise, Distributed Source Compression and Slepian-Wolf Theorem

INFOTH Note 22: AWGN Channel and Shannon Limit

Waveform Channel Model, Additive White Gaussian Noise with its Shannon Theorem, and Spectral Efficiency

INFOTH Note 21: Polar Codes and Related Theories (Bhattacharyya, Martingale)

Polar Codes, Bhattacharyya Parameter, Martingale Theory, and Channel Polarization

INFOTH Note 20: Channel Coding Schemes

Maximum Likelihood Decoding, Block Coding (Binary Linear, Hamming, etc.) & Polar Codes

INFOTH Note 19: Channel Coding Theorem for DMC 2

Proof of Shannon's Channel Coding Theorem, Converse Theorem

INFOTH Note 18: Channel Coding Theorem for DMC 1

Discrete Memoryless Channel, Shannon's Channel Coding Theorem (2nd)

INFOTH Note 9: Differential Entropy

Differential Entropy (for Real-valued/Continuous R.V.s)

INFOTH Note 8: BW Transform

Burrows-Wheeler Transform

INFOTH Note 7: Huffman & Arithmetic Coding

Huffman Coding and Arithmetic Coding

INFOTH Note 6: Kraft's Inequality, Shannon Code

Codes, Kraft's Inequality, Shannon Code