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The following is a tentative schedule of what we will cover in class when. Check this space regularly for updates.
Last update: 05-25-22
Key to reading materials:
| Date | Topics | Readings | Assignments Due (11:59 PM) |
|---|---|---|---|
| M 10/04 | What is (Probabilistic) Machine Learning? | MLPP 1.1-1.3, BMRL 13.1 | – |
| [PART I: Machine Learning Fundamentals | (~ 2 weeks)] | ||
| W 10/06 | Nearest Neighbor Classification | FCML 5.3.1, BRML 14.1-14.2 | Background Survey |
| Evaluating a Classifier | |||
| F 10/08 | Cross-Validation | FCML 1.5 | – |
| M 10/11 | Linear Regression: One Scalar Predictor | FCML 1.1-1.4 | – |
| W 10/13 | Linear Regression: Vector Predictor | HW0: Coding Warmup | |
| F 10/15 | Linear Regression: Vector Predictor (cont’d) | – | |
| M 10/18 | Linear Regression: Polynomials as Linear Models | – | |
| W 10/20 | Linear Regression: Regularization | HW1: Classification | |
| F 10/22 | Linear Regression: Regularization (cont’d) | ||
| [PART II: Probability Fundamentals | (~ 2 weeks)] | ||
| M 10/25 | Probability: Probability Spaces | FCML 2.1 | – |
| W 10/27 | Probability: Random Variables | FCML 2.2, BRML 1.1, 8.1-8.3 | HW2: Regression |
| F 10/29 | Probability: Discrete Random Variables | FCML 2.2, BRML 1.1, 8.1-8.3 | – |
| M 11/01 | Probability: Joint and Conditional Probability | FCML 2.3-2.6, BRML 1.1, 8.1-8.3 | – |
| W 11/03 | Probability: Continuous Random Variables | FCML 2.3-2.6, BRML 1.1, 8.1-8.3 | – |
| F 11/05 | Probability: Continuous Random Variables | – | – |
| Sun 11/07 | HW3: Probability Basics | ||
| M 11/08 | Probablity: Expectation and Variance | – | – |
| [PART III: Inference Fundamentals (~ 3 weeks)] | |||
| W 11/10 | Maximum Likelihood Estimation | FCML 2.7-2.8, BRML 8.6, 8.8.1 | – |
| F 11/12 | Bias/Variance Decomposition | FCML 2.9-2.11 | – |
| Sun 11/14 | HW4: Maximum Likelihood | ||
| M 11/15 | Bias and Variance in Regression | FCML 2.9-2.11 | – |
| W 11/17 | Bayesian Inference: Fundamentals | FCML 3.1-3.3, BRML 1.2-1.4 | – |
| F 11/19 | Bayesian Inference: Conjugate Priors | – | |
| Sun 11/21 | HW5: Bayesian Fundamentals | ||
| THANKSGIVING BREAK | |||
| M 11/29 | Belief Networks and Conditional Independence | FCML 3.6, BRML 3.1-3.3 | – |
| W 12/01 | Bayesian Inference: Model Averaging and Occam’s Razor | – | |
| [PART IV: Bayesian Supervised Learning | (~ 2 weeks)] | ||
| F 12/03 | Naive Bayes Classification: Formulation and Max Likelihood | FCML 5.1-5.2.1.6, BRML 10.1-10.2 | – |
| Sun 12/05 | HW6: Bayesian Inference and Belief Networks | ||
| M 12/06 | Bayesian Naive Bayes I: Posterior Inference | FCML 5.2.1.7, BRML 10.3 | – |
| W 12/08 | Bayesian Naive Bayes II: Prediction and Classification | – | Exam Distributed |
| F 12/10 | Bayesian Regression I: Posterior Inference | FCML 3.8, BRML 12.1-12.4 | – |
| Sun 12/12 | Exam Due | ||
| M 12/13 | Bayesian Regression II: Prediction and Model Selection | FCML 3.9, BRML 12.1-12.4 | – |
| [PART V: Unsupervised Learning and Latent Variable Models | (~2 weeks)] | ||
| W 12/15 | Clustering I: K-Means | FCML Ch. 6.1-6.2 | – |
| F 12/17 | Clustering II: Mixture Models | FCML 6.3.1-6.3.2, BRML 20.1 | – |
| M 12/20 | Clustering III: The EM Algorithm | FCML 6.3.3-6.3.8, BRML 20.2-20.3 | HW7: Bayesian Classification and Regression |
| W 12/22 | Video – Clustering IV: Gibbs Sampling | FCML 6.3.9, 9.1-9.2, 10.1 | – |
| WINTER BREAK | |||
| M 01/03 | Review and Refresher | – | |
| W 01/05 | Hidden Markov Models: Overview | BRML 23.1-23.3, 23.5 | Clear Project Topic With Me |
| F 01/07 | HMMs: Prediction and Inference | – | |
| M 01/10 | HMMs: Learning with EM | – | |
| W 01/12 | HMMs: Bayesian Learning | HW9: Hidden Markov Models | |
| F 01/14 | Nonparametric Models | – | |
| Sun 01/23 | FINAL PRESENTATIONS (2-4 PM) | Final Project Writeup |