COMSM0075

COMSM0075

Week 1 / 2 - CH - information theory

lecture notes for information theory pdf
lecture notes for information in the brain pdf
lecture notes for differential entropy pdf
lecture notes for infomax pdf

lecture 1 motivating Shannon's entropy youtube / slides (pdf)
lecture 2 Shannon's entropy youtube / slides (pdf)
lecture 3 joint and conditional entropy youtube / slides (pdf)
lecture 4 mutual information youtube / slides (pdf)
lecture 5 the data processing inequality youtube / slides (pdf)
lecture 6 information in the brain youtube / slides (pdf)
lecture 7 differential entropy examples youtube / slides (pdf)
lecture 8 differential entropy and Shannon's entropy youtube / slides (pdf)
lecture 9 mutual information for continuous probabilities youtube / slides (pdf)
lecture 10 the cocktail party problem youtube / slides (pdf)
lecture 11 infomax youtube / slides (pdf)

worksheet 1 pdf, solution pdf
worksheet 2 pdf, solution pdf
worksheet 3 pdf
textbook - Element of Information Theory by Cover and Thomas pdf at cs-114.org

Week 3 - CH - the Bayesian Brain / Kalman filters

lecture notes for the Bayesian brain pdf
lecture notes for the Kalman filter pdf

lecture 1 the Bayesian approach youtube / slides (pdf)
lecture 2 Bayesian fusion youtube / slides (pdf)
lecture 3 The Kalman filter youtube / slides (pdf)

Week 4 - RPC - Neural circuits and learning

L1: Neural circuits and learning: introduction Stream / slides (pdf)
L2: Supervised learning and backprop Stream / slides (pdf)
Lab 1: Supervised learning and backprop (link; solutions)

Week 5 - RPC - Neural circuits and learning

L3: Visual system: deep learning? Stream / slides (pdf)
L4: Reinforcement learning Stream / slides (pdf)
Lab 2: Reinforcement learning (link; solutions)

Week 6 - RPC - Neural circuits and learning

L5: Unsupervised learning Stream / slides (pdf)
L6: Temporal processing and RNNs Stream / slides (pdf)

Week 7 - RPC - Neural circuits and learning

L7: Gated RNNs, Brain vs Maquina Stream / slides (pdf)