| Date | Subject | Reading |
|---|
| On your own | learn linear algebra | Self Test, Self Test Solutions, Strang’s Course |
| W/Aug 31 | Intro, intro_slides | |
| F/Sep 2 | Probability Review | Bishop 1.1-1.4, slides |
| M/Sep 5 | No class: Labor Day | |
| W/Sep 7 | Nearest Neighbor norm slides | Bishop 2.5 |
| F/Sep 9 | Intro to Matlab Tutorial | Coursera octave tutorial |
| M/Sep 12 | Decision Trees (and information theory) slides | Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 |
| W/Sep 14 | Basic Point Estimation (MLE andMAP) PDF slides | Bishop 2.1, Appendix B, MLMath covariance |
| F/Sep 16 | Gaussians,1-D Regression slides | Bishop 1.2.4, Bishop 2.3.1-2.3.3 optional slides |
| M/Sep 19 | Regression more_regression | Bishop 1.1-1.4, Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2 |
| W/Sep 21 | Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for Regression | Bishop 1.3, 1.5, 3.2 applet |
| F/Sep 23 | regression penalty slides Stepwise, streamwise, stagewise | Hastie et al. 7.1-7.3 (supplemental reading:LASSO) |
| M/Sep 26 | Classification Naive Bayes slides
| |
| W/Sep 28 | Logistic Regression | Naive Bayes vs Logistic Regression, Bishop 4.0, 4.2- 4.5 |
| F/Sep 30 | NB vs. LR, Basis Functions, RBFs robust regression | short videos on hat matrix and stepwise regression and RBFs |
| M/Oct 3 | MDL slides, MDL feature selection review | Hastie et al. 7.5-7.8 |
| W/Oct 5 | Boosting | Bishop 14.3 , Schapire’s Tutorial
|
| F/Oct 7 | No class: Fall Break | |
| M/Oct 10 | Neural Net slides Supervised Deep Networks | supplemental: deep net tutorial |
| W/Oct 12 | more deep learning | |
| R/Oct 13 | review session 5:00 p.m. meyerson Room: B(asement) | Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency |
| F/Oct 14 | Midterm | sample exam questions and answers more examples are part of the old final exam below |
| M/Oct 17 | Kernel Methods kernel examples kernel regression
| Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels |
| W/Oct 19 | More Kernels, Lagrange Duality Lagrange examples | Bishop Appendix E LagrangeMultipliers video working an example |
| F/Oct 21 | Support Vector Machines
| Bishop 7.1 (Max Margin) Hearst 1998 |
| M/Oct 24 | SVM slides
| Burges 1998 |
| W/Oct 26 | Perceptron slides | supplemental reading:MIRA, Perceptrons and SVM Recap and Optimization |
| F/Oct 28 | Vectors, Matrices, Eigenvectors | Kosecka’s review slides |
| M/Oct 31 | Dim. Reduction PCA eigenwords | Bishop Appendix C Properties of Matrices |
| W/Nov 2 | Unsupervised Deep Networks | |
| F/Nov 4 | PCR, PLS and CCA slides more courses | Bishop 12.1 supplemental slides |
| M/Nov 7 | Unsupervised Learning: Clustering, K-means EM | Bishop 9.1-9.3 |
| W/Nov 9 | EM Generative PCA | Bishop 12.1–12.3, supplemental:Neal and Hinton |
| F/Nov 11 | LDA slides | supplemental:LDA intro and original LDA paper |
| M/Nov 14 | Generative model summary, loss functions and ML speed slides | |
| W/Nov 16 | Netflix | supplemental:netflix |
| F/Nov 18 | Project overview and advice and project slides; Real world machine learning | More advice and python for converting text to ints |
| M/Nov 21 | Bayes Nets | supplemental: Koller+al, Graphical Models in a Nutshell |
| W/Nov 23 | no class | |
| F/Nov 25 | No class — Happy Thanksgiving!! | |
| M/Nov 28 | Bayes Net construction | Bishop 8.2 |
| W/ Nov 30 | Bayes Net Inference Hidden Markov Models and HMM slides | Rabiner’s HMM Tutorial, supplemental:Bishop 13.1–2 |
| F/Dec 2 | Recurrent Neural Networks | |
| M/Dec 5 | Active learning and Causality | |
| W/Dec 7 | Big Data | Unreasonable effectiveness of data |
| F/Dec 9 | The Future of ML and humanity | |
| M/Dec 12 | Final project awards; Final Review Other material Review Questions
| LIONbook - a quick review; After CIS520 |
| W/Dec 21 | Review Session: 4:00–5:00 pm Building: DRLB Room: A1 | |
| Th/Dec 22 | Final: 9:00am-11:00am FAGN AUD(in the Nursing school!!) | 2014 final and solution |
