Time and Location:
Monday, Wednesday 9:30am-10:50am, NVIDIA Auditorium
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. Please check back soon.
Event | Date | Description | Materials and Assignments | |
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Lecture 1 | 9/23 | Introduction and Basic Concepts | ||
Lecture 2 | 9/25 | Supervised Learning Setup. Linear Regression. |
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Assignment | 9/26 | Problem Set 0. Due Wednesday, Oct 2 at 11:59pm | ||
Section 1 | 9/28 | Friday Lecture: Linear Algebra. | Notes | |
Lecture 3 | 9/30 |
Weighted Least Squares. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. |
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Lecture 4 | 10/2 | |||
Assignment | 10/2 | Problem Set 1. Due Wednesday, Oct 16 at 11:59pm | ||
Section 2 | 10/4 | Friday Lecture: Probability | Notes | |
Lecture 5 | 10/7 | Gaussian Discriminant Analysis. Naive Bayes. | ||
Lecture 6 | 10/9 |
Laplace Smoothing. Support Vector Machines. |
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Section 3 | 10/11 | Friday Lecture: Python and Numpy | Notes | |
Lecture 7 | 10/14 | Support Vector Machines. Kernels. | ||
Lecture 8 | 10/16 | Neural Networks - 1 | Class Notes | |
Assignment | 10/16 |
Problem Set 2. Due Wednesday, Oct 30 at 11:59pm
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Section 4 | 10/18 | Friday Lecture: Evaluation Metrics |
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Project | 10/18 | Project proposal due 10/18 at 11:59pm. | ||
Lecture 9 | 10/21 | Neural Networks - 2 | ||
Lecture 10 | 10/23 | Bias - Variance. Regularization. Feature / Model selection. |
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Section 5 | 10/25 | Friday Lecture: Deep Learning |
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Lecture 11 | 10/28 | Practical Advice for ML projects. |
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Assignment | 10/30 |
Problem Set 3. Due Wednesday, Nov 13 at 11:59pm |
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Lecture 12 | 10/30 | K-Means. GMM (non EM). Expectation Maximization. | Class Notes | |
Section 6 | 11/1 | Friday Lecture: Midterm Review |
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Lecture 13 | 11/4 | Expectation Maximization. Factor Analysis. |
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Midterm | 11/5 | The midterm details are posted on Piazza. | ||
Lecture 14 | 11/6 | Principal and Independent Component Analysis. | Class Notes | |
Section 7 | 11/8 | Friday Lecture: Decision Trees. Boosting. Bagging. | Class Notes | |
Lecture 15 | 11/11 | Weak Supervision |
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Lecture 16 | 11/13 | |||
Assignment | 11/13 |
Problem Set 4. Due Wednesday, Dec 4 at 11:59pm |
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Section 8 | 11/15 | Friday Lecture: On critiques of Machine Learning |
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Project | 11/15 | Project milestones due 11/15 at 11:59pm. | ||
Lecture 17 | 11/18 | Value Iteration and Policy Iteration |
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Lecture 18 | 11/20 | Bias and Variance |
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Lecture 19 | 12/2 | Learning Theory |
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Lecture 20 | 12/4 | Course wrap-up. Beyond CS229 Guest Lectures! Details [link] | ||
Project | 12/11 | Poster submission deadline, due 12/11 at 11:59pm (no late days). | ||
Project | 12/12 | Poster presentations from 8:30-11:30am. Venue and details to be announced. | ||
Project | 12/13 | Project final report due 12/13 at 11:59pm (no late days). | ||
Supplementary Notes | ||||
Other Resources
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