# click the upper-left icon to select videos from the playlist
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
1. Clicking ▼&► to (un)fold the tree menu may facilitate locating what you want to find. 2. Videos embedded here do not necessarily represent my viewpoints or preferences. 3. This is just one of my several websites. Please click the category-tags below these two lines to go to each independent website.
No comments:
Post a Comment