| Date (Lec/Rec) | Subject | Reading |
|---|
| Lec: W/Sep 9 | Intro; Basic Point Estimation; (TA Notes) | Andrew Moore's basic tutorial on probabilities (pages 1-25), Bishop 2.1, Appendix B, MLMath |
| Rec: F/Sep 11 | Matlab Tutorial | |
| Lec: M/Sep 14 | Gaussians, Regression | Bishop 1.1 to 1.4, Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2 |
| Lec: W/Sep 16 | Bias Variance Decomposition | Bishop 1.3, 1.5, 3.2 |
| Rec: F/Sep 18 | MLE/MAP for Multivariate Gaussians, Bias/Variance for Regression | |
| Lec: M/Sep 21 | Classification, Generative vs Discriminative, Naive Bayes | Naive Bayes vs Logistic Regression by T. Mitchell, Bishop 4.0, 4.2, 4.3, 4.4, 4.5 |
| Lec: W/Sep 23 | Logistic Regression, Decision Trees; (TA Notes 2) | Decision Trees by N. Nilsson, Bishop 1.6, 14.4 |
| Rec: F/Sep 25 | Decision boundaries, Naive Bayes vs. LR, Basis Functions | |
| Lec: M/Sep 28 | Decision Trees cont., Overfitting, Regularization and Cross-validation | Bishop 1.3, 3.1.4, Optional reading: R. Kohavi's, A Study of Cross-Validation and Bootstrap |
| Lec: W/Sep 30 | Boosting | Bishop 14.3, Schapire's Tutorial |
| Rec: F/Oct 2 | Decision Trees, Cross-Validation, Boosting | |
| Lec: M/Oct 5 | Non-parametric Methods, Nearest Neighbor | Bishop 2.5 |
| Lec: W/Oct 7 | Kernels Methods | Bishop 6.1,6.2 (Kernels) Bishop 7.1 (Max Margin) |
| Rec: F/Oct 9 | More Kernels, Lagrange Duality Tutorial | |
| Lec: M/Oct 12 | Support Vector Machines | Hearst 1998, Burges 1998 |
| Lec: W/Oct 14 | Generalization Bounds | Goldman's COLT survey, sections 1-3.1, A. Blum's handout on tail inequalities |
| Rec: F/Oct 16 | Midterm Review | |
| Lec: M/Oct 19 | Happy Fall Break! | |
| W/Oct 21 | Midterm | Slogans |
| Lec: F/Oct 23 | Generalization Bounds: VC dimension | |
| Lec: M/Oct 26 | Online Learning (Guest lecturer: Sasha Rakhlin) | Bishop 4.1.7, Blum's Online Learning Survey |
| Rec: W/Oct 28 | Midterm solutions review | |
| Lec: F/Oct 30 | Unsupervised Learning: Clustering, K-means | Bishop 9.1-9.3 |
| Rec: M/Nov 2 | Caveats of k-means, Intro to SVD | |
| Lec: W/Nov 4 | EM | Neal and Hinton |
| Lec: F/Nov 6 | Canceled | |
| Lec: M/Nov 9 | EM wrap-up, Dim. Reduction: PCA | Bishop 12.1-12.3 |
| Lec: W/Nov 11 | PCA wrap-up | |
| Rec: F/Nov 13 | Project Overview and Advice | Project webpage |
| Lec: M/Nov 16 | Canceled | Bishop 8.1-8.2 |
| Lec: W/Nov 18 | Directed Graphical Models: Bayes Nets | Bishop 8.4.1-8.4.2 |
| Rec: F/Nov 20 | More Project Advice | |
| Lec: M/Nov 23 | Inference in Bayes Nets | Koller+al, Graphical Models in a Nutshell |
| Lec: W/Nov 25 | Inference in Bayes Nets | |
| Rec: F/Nov 27 | Happy Thanksgiving!! | |
| Lec: M/Nov 30 | Hidden Markov Models | Rabiner's HMM Tutorial |
| Lec: W/Dec 2 | Hidden Markov Models | |
| Rec: F/Dec 4 | $1 million machine learning | Project Reports |
| Lec: M/Dec 7 | Final Review | |
| Lec: W/Dec 9 | Canceled | |
| W/Dec 16 | Final in DRLB A2 ! | |