COMSM0075

COMSM0075

Week 1 - CH - information theory

lecture notes for information theory 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)

Week 2 - CH - information theory in the brain

worksheet 1 (based on week 1). pdf, solution pdf
lecture notes for information in the brain pdf
lecture notes for differential entropy pdf
lecture notes for infomax 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)

Week 3 - CH - the Bayesian Brain / Kalman filters

Due to illness I haven't been able to update the notes from last year, can you look at the week 3 notes and remaining week 1/2 worksheets in last years guide 2020-21 and I will hope to update it when I recover.

Week 4 - RPC - Neural circuits and learning

L1: Neural circuits and learning: introduction Stream / slides (pdf)
L2: Supervised learning and backprop Stream / slides (pdf)

Week 5 - RPC - Neural circuits and learning

Lab 1: Supervised learning and backprop (link; solutions)
L3: Visual system: deep learning? Stream / slides (pdf)
L4: Reinforcement learning Stream / slides (pdf)

Week 6 - Reading week

Week 7 - RPC - Neural circuits and learning

Lab 1+2: Reinforcement learning (Lab 2: link; solutions)
L5: Unsupervised learning Stream / slides (pdf)
L6: Temporal processing and RNNs Stream / slides (pdf)

Week 8 - RPC - Neural circuits and learning

Lab 2: Reinforcement learning (solutions)
L7: Gated RNNs, Brain vs Maquina Stream / slides (pdf)

Week 9 - 11 Coursework weeks