01 (10/04): Introduction
02-03 (10/06-10/08): K Nearest Neighbors / Cross-Validation
04 (10/11): Linear Regression I: One Predictor
05-06 (10/13-10/15): Linear Regression II: Multiple Predictors
07-09 (10/18-10/22): Linear Regression III: Polynomials and Regularization
10-11 (10/25-10/29): Probability I: Probability Spaces / Random Variables
11-12 (10/29-11/01): Probability II: Discrete Random Variables / Joint and Conditional Probability
13-14 (11/03-11/08): Probability III: Continuous Random Variables / Expectation and Variance
15 (11/10): Maximum Likelihood Estimation
16 (11/12): Bias-Variance Tradeoff
17 (11/15-11/17): Bias and Variance in Regression
18 (11/17-11/29): Bayesian Inference I: Fundamentals
19 (11/29-12/01): Bayesian Inference II: Conjugate Priors
(we skipped) Bayesian Inference III: Conjugate Priors and Exponential Families
22 (12/03): Bayesian Inference IV: Prediction and Model Selection
23 (12/10-12/13): Belief Networks
24 (12/13-12/15): Naive Bayes Classifier I: Formulation and MLE Learning
25 (4/13-4/15): Naive Bayes Classifier II: Bayesian Naive Bayes
27 (4/24): Clustering I: The K-means algorithm
28 (4/27): Clustering II: Mixture Models and the EM Algorithm
33 (01/14): Gaussian Process Regression
25 (4/17): Bayesian Regression I: Posterior Inference (typos not yet corrected)
26 (4/20(-4/22?)): Bayesian Regression II: Prediction and Model Selection
29-30 (4/29-5/01): Clustering III and IV: EM Analysis / Bayesian Clustering
31 (5/04): Hidden Markov Models I: Intro and Efficient Marginalization
32 (5/06): Hidden Markov Models II: Parameter Learning With EM
33 (5/08): Hidden Markov Models III: Bayesian Parameter Learning With Gibbs
Gaussian Process Classification
Infinite Mixture Model (Nonparametric Clustering)