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source: JHU Learning Theory 2014年5月1日
Johns Hopkins Learning Theory (Spring 2014)
Lecture 1 (Intro. & Perceptron) 54:36
Lecture 2 (Probability Theory) 1:05:54
Lecture 3 (Normal Equation) 1:14:17
Lecture 4 (Newton Raphson) 1:11:17
Lecture 5 (Generalization) 52:14
Lecture 6 (Maximum Likelihood) 1:06:22
Lecture 7 (Scalar Kalman Filter) 1:08:48
Lecture 8 (State Dependent Noise) 52:21
Lecture 9 (Gaussian Factorization) 58:09
Lecture 10 (Causal Inference) 1:17:34
Lecture 11 (Generative Models of Learning) 1:05:47
Lecture 12 (Kamin Blocking) 55:08
Lecture 13 (Adaptive Error Sensitivity) 54:12
Lecture 14 (Multi-state models of learning) 1:08:30
Lecture 15 (Subspace Analysis) 1:01:27
Lecture 16 (Expectation Maximization) 1:07:45
Lecture 17 (Intro. to Cost & Reward in Movement) 1:11:56
Lecture 18 (Lagrange Multipliers) 1:20:31
Lecture 19 (Bellman Eq.) 1:08:13
Lecture 20 (Optimal Control in Linear Systems) 1:14:15
Lecture 21 (Optimal Control with Signal Dependent Noise) 1:03:17
Lecture 22 (Fisher LDA & Bayesian Classification) 1:00:38
Lecture 23 (Linear & Quadratic Decision Boundaries) 46:06
Guest Lecture (Optimal Control of Saccades in Ataxia Telangiectasia) 48:21
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