| Date | Subject | Reading |
|---|
| On your own | learn linear algebra, basic probability | Self Test, Self Test Solutions, Strang’s Course Probability Review Bishop 1.1-1.4, slides |
| W/Aug 30 | Intro, intro_slides | |
| F/Sep 1 | Intro to Matlab Tutorial | Coursera octave tutorial |
| M/Sep 4 | No class: Labor Day | |
| W/Sep 6 | Local learning Norm slides | Bishop 2.5 |
| F/Sep 8 | Decision Trees (and information theory) slides | Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 KL and mutual info |
| M/Sep 11 | Basic Point Estimation (MLE and MAP) PDF slides | Bishop 2.1, Appendix B, MLMath covariance |
| background | Gaussians | Bishop 1.2.4, Bishop 2.3.1-2.3.3 |
| W/Sep 13 | Regression, Regression slides | 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 |
| F/Sep 15 | Classification Logistic Regression, Naive Bayes slides
| Bishop 4.0, 4.2- 4.5 |
| M/Sep 18 | NB vs. LR, Basis Functions, RBFs | Naive Bayes vs Logistic Regression, short video: RBFs |
| W/Sep 20 | Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for Regression | Bishop 1.3, 1.5, 3.2 applet |
| F/Sep 22 | regression penalty slides Stepwise, streamwise, stagewise | short video:stepwise regression,Hastie et al. 7.1-7.3 LASSO (supplemental reading) |
| M/Sep 25 | MDL slides, MDL feature selection review | MDL-supplemental MDL-background |
| W/Sep 27 | Neural Net slides Supervised Deep Networks | supplemental: deep net tutorial |
| F/Sep 29 | more deep learning Supervised Deep Networks | supplemental: gay learning |
| M/Oct 2 | Constrained Optimization and Lagrangian Duality | |
| W/Oct 4 | Support Vector Machines
| Additional notes, Bishop 7.1 (Max Margin), Hearst 1998 |
| F/Oct 6 | No class: Fall Break | |
| M/Oct 9 | Kernel Methods
| Additional notes, Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels |
| W/Oct 11 | Online Learning and Perceptron | Additional notes |
| F/Oct 13 | Boosting | Additional notes, Bishop 14.3, Schapire’s Tutorial
|
| M/Oct 16 | Performance Measures | |
| T/Oct 17 | review session 5:00 p.m. ANNS Room: 110 | Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency |
| W/Oct 18 | Midterm recitation recording | sample exam questions and answers more examples are part of the old final exam below |
| F/Oct 20 | No class | |
| M/Oct 23 | Learning Theory: A Brief Primer | |
| W/Oct 25 | Vectors, Matrices, SVD SVD slides | Kosecka’s review slides |
| F/Oct 27 | Dim. Reduction PCA eigenwords | Bishop 12.1: PCA Bishop Appendix C Properties of Matrices PCR, PLS and CCA |
| M/Oct 30 | Unsupervised Deep Networks more courses |
| W/Nov 1 | Unsupervised Learning: Clustering, K-means EM | Bishop 9.1-9.3 |
| F/Nov 3 | EM Generative PCA,LDA slides | Bishop 12.1–12.3, supplemental:Neal and Hintonsupplemental:LDA intro and original LDA paper |
| M/Nov 6 | Netflix | supplemental:netflix |
| W/Nov 8 | Project overview and advice and project slides; Real world machine learning | More advice and python for converting text to ints |
| F/Nov 10 | Hidden Markov Models | supplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2 |
| M/Nov 13 | Hidden Markov Models | supplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2 |
| W/Nov 15 | Bayesian Networks | Jordan book draft Ch 2 (Sec 2.1), additional notes, supplemental: Koller et al., Graphical Models in a Nutshell |
| F/Nov 17 | Markov Networks; Variable Elimination | Jordan book draft Ch 2 (Sec 2.2) and Ch 3; supplemental: Koller et al., Graphical Models in a Nutshell |
| M/Nov 20 | Sum-Product, Max-Sum | Jordan book draft Ch 4 (Sec 4.1, 4.3), supplemental: Jordan book draft Ch 17, Koller et al., Graphical Models in a Nutshell |
| W/Nov 22 | No class | |
| F/Nov 24 | No class — Happy Thanksgiving!! | |
| M/Nov 27 | Structured Prediction | supplemental: Lafferty et al., 2001 (CRFs), McCallum, 2003 (more CRFs), Taskar et al., 2003 (StructSVM), Collins et al., 2008 (EG algorithms for CRFs and StructSVM), Joachims et al., 2009 (Cutting-plane algorithm for StructSVM) |
| W/Nov 29 | Semi-Supervised Learning, Active Learning | supplemental: Nigam et al., 2000 (EM for semi-supervised learning), Zhu’s semi-supervised learning survey, Settles’ active learning survey |
| F/Dec 1 | More deep learning, Killian’s lecture | |
| M/Dec 4 | Reinforcement Learning | supplemental: additional reinforcement learning exercise |
| W/Dec 6 | Big Data | Unreasonable effectiveness of data |
| F/Dec 8 | The Future of ML and humanity | supplemental: job futures |
| M/Dec 11 | Final project awards; Other material Dynamic Nets current deep learning
| LIONbook - a quick review; After CIS520 |
| W/Dec 13 | Review Session: 4:00–5:00 pm Building: ANNS 110 | Final Review Review Questions audio recording (under canvas announcements) |
| T/Dec 19 | Final: 9:00am-11:00am CHEM 102 | 2014 final and solution and 2016 final and solution |
