Week | Date | Content | Prep & Assignments |
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1 | Tue 01/22 | Lecture 1: Introduction and quick review of math concepts |
Week 1 Reading and Guide HW 0 (Try out Gradescope, due 01/30 6pm) |
Thu 01/29 | Lecture 2: Problem formulation with a case study and data collection |
HW 1 (Matrix algebra, Due 02/06) |
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2 | Mon 01/28 | Lab | |
Tue 01/29 | Lecture 3: Introduction to PCS, internal and external validity |
Week 2 Guide |
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Thu 01/31 | Lecture 4: Cross-validation (CV), PQR-S | ||
3 | Mon 02/04 | Lab | |
Tue 02/05 | Lecture 5: Data preprocessing |
Week 3 Guide |
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Thu 02/07 | Lecture 6: Exploratory data analysis (EDA) |
HW 2 (PCA, Due 02/20) |
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4 | Mon 02/11 | Lab | |
Tue 02/12 | Lecture 7: EDA continued: PCA, K-means |
Week 4 Guide |
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Thu 02/14 | Lecture 8: EDA continued: other unsupervised learning methods | ||
5 | Mon 02/18 | Lab | |
Tue 02/19 | Lecture 9: K-means, GMM and EM |
Week 5 Guide |
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Thu 02/21 | Lecture 10: Linear regression |
Project 1 (EDA on Redwoord data, Due 03/08) + Short HW3 |
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6 | Mon 02/25 | Lab | |
Tue 02/26 | Lecture 11: Regularizations in linear regression |
Week 6 Guide |
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Thu 02/28 | Lecture 12: Regularizations in linear regression | ||
7 | Mon 03/04 | Lab | |
Tue 03/05 | Lecture 13: Model selection |
Week 7 Guide |
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Thu 03/07 | Lecture 14: Model Assessment |
HW 4 (Linear regression, TBA, Due 03/20) |
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8 | Mon 03/11 | Lab | |
Tue 03/12 | Lecture 15: Bias-variance trade-off |
Week 8 Guide |
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Thu 03/14 | Lecture 16: Classification: introduction | ||
9 | Mon 03/18 | Lab | |
Tue 03/19 | Lecture 17: Classification: introduction |
Week 9 Guide |
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Thu 03/21 | Midterm in class |
HW 5 (Classification, TBA, Due 04/03) |
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10 | Tue 03/26 | Spring break | |
Thu 03/28 | Spring break | ||
11 | Mon 04/01 | Lab | |
Tue 04/02 | Lecture 18: Classification: logistic regression |
Week 11 Guide |
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Thu 04/04 | Lecture 19: Classification: Support vector machine (SVM) |
Project 2 (Classification on cloud data, Due 04/17) |
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12 | Mon 04/08 | Lab | |
Tue 04/09 | Lecture 20: Kernel methods |
Week 12 Guide |
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Thu 04/11 | Lecture 21: Kernel methods | ||
13 | Mon 04/15 | Lab | |
Tue 04/16 | Lecture 20: Nearest Neighbor |
Week 13 Guide |
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Thu 04/18 | Lecture 21: Tree-based methods |
HW 6 (Nonlinear methods, Due 04/31) |
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14 | Mon 04/22 | Lab | |
Tue 04/23 | Lecture 22: Boosting algorithms |
Week 14 Guide |
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Thu 04/25 | Lecture 23: Neural networks | ||
15 | Mon 04/29 | Lab | |
Tue 04/30 | Lecture 24: Interpretable machine learning |
Week 15 Guide |
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Thu 05/02 | Lecture 25: Final Review | ||
16 | Tue 05/07 | RRR Week | |
Thu 05/09 | RRR Week | ||
17 | Mon 05/13 | FINAL EXAM TBA | |