Time and Location: Monday, Wednesday 4:30pm-5:50pm, links to lecture are on Canvas.
Class Videos: Current quarter's class videos are available here for SCPD students and
here for non-SCPD students.
Note: This is being updated for Spring 2020. The dates are subject to change as we figure out deadlines. Please check back soon.
| Week | Event | Date | Description | Materials |
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| Week 1 | Lecture 1 | 4/6 | Introduction and Basic Concepts | Slides |
| Lecture 2 | 4/8 | Supervised Learning Setup. Linear Regression. | Class Notes | |
| Assignment | 4/8 | Problem Set 0. Due 4/15 at 11:59pm. | ||
| Section 1 | 4/10 | Friday Lecture: Linear Algebra. |
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| Week 2 | Lecture 3 | 4/13 |
Weighted Least Squares. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. |
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| Lecture 4 | 4/15 |
Class Notes
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| Assignment | 4/15 | Problem Set 1. Due 4/29 at 11:59pm. | ||
| Section 2 | 4/17 | Friday Lecture: Probability |
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| Week 3 | Lecture 5 | 4/20 | Gaussian Discriminant Analysis. Naive Bayes. Laplace Smoothing. | Class Notes |
| Lecture 6 | 4/22 |
Laplace Smoothing. Support Vector Machines. |
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| Section 3 | 4/24 | Friday Lecture: Python and Numpy |
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| Project | 4/24 | Project proposal due 4/24 at 11:59pm. | ||
| Week 4 | Lecture 7 | 4/27 | Support Vector Machines. Kernels. |
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| Lecture 8 | 4/29 | Neural Networks - 1 |
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| Assignment | 4/29 |
Problem Set 2. Due 5/13 at 11:59pm.
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| Section 4 | 5/1 | Friday Lecture: Evaluation Metrics |
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| Week 5 | Lecture 9 | 5/4 | Neural Networks - 2 |
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| Lecture 10 | 5/6 | Bias - Variance. Regularization. Feature / Model selection. | Class Notes | |
| Section 5 | 5/8 | Friday Lecture: Deep Learning |
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| Week 6 | Lecture 11 | 5/11 | K-Means. GMM (non EM). Expectation Maximization. | Class Notes |
| Lecture 12 | 5/13 | Expectation Maximization (continued) |
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| Assignment | 5/13 |
Problem Set 3. Due 5/27 at 11:59pm. |
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| Section 6 | 5/15 | Friday Lecture: Midterm Review |
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| Project | 5/15 | Project milestones due 5/15 at 11:59pm. | ||
| Week 7 | Lecture 13 | 5/18 | Factor Analysis. |
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| Midterm | 5/20 | See details at Piazza post | ||
| Lecture 14 | 5/20 | Principal and Independent Component Analysis. |
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| Week 8 | Lecture 15 | 5/25 | Memorial Day, no lecture. | |
| Lecture 16 | 5/27 | Weak Supervision |
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| Assignment | 5/27 |
Problem Set 4. Due 6/10 at 11:59pm (no late days). |
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| Week 9 | Lecture 17 | 6/1 | Markov Decision Process. Value Iteration and Policy Iteration. Q-Learning. Value function approximation. |
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| Lecture 18 | 6/3 | Reinforcement Learning continued | ||
| Week 10 (Last Week of class) | Lecture 19 | 6/8 | Policy search. Reinforce. POMDPs. |
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| Lecture 20 | 6/10 | Recap, Fairness, Adversarial |
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| Project | 6/10 | Poster PDF and video presentation. Due 6/10 at 11:59pm (no late days). | ||
| Project | 6/10 | Project final report. Due 6/10 at 11:59pm (no late days). | ||
| Supplementary Notes Optional Topics | ||||
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