| 30-Aug |
No Lecture |
Lecture 1: Introduction to CS109A |
Lab 1: Data - formats| sources| & scraping |
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| 6-Sep |
No Lecture (Labor Day) |
Lecture 2: Introduction to PANDAS and EDA |
Lab 2: Pandas & EDA 2 |
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R:HW1 - D:HW0 |
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| 13-Sep |
Lecture 3: Introduction to Regression kNN and Linear Regression |
Lecture 4: Multi-linear and Polynomial Regression |
Lab 3: kNN & Linear Regression |
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R:HW2 - D:HW1 |
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| 20-Sep |
Lecture 5: Model Selection and Cross Validation |
Lecture 6: Regularization Ridge and Lasso Regression |
Lab 4: Multiple Regression & Polynomial Regression |
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| 27-Sep |
Lecture 7: Probability |
Lecture 8: Inference in Regression and Hypothesis Testing |
Lab 5: Estimation of Regulariztion Coeffs /w CV |
Advanced Section 1: Linear Algebra Primer |
R:HW3 - D:HW2 |
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| 4-Oct |
Lecture 9: Missing Data & Imputation |
Lecture 10: Principal Component Analysis |
Lab 6: PCA |
Advanced Section 2: Hypothesis Testing |
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| 11-Oct |
No Lecture (Indigenous Peoples' Day) |
Lecture 11: Case Study |
Midterm |
Advanced Section 3: Math Foundations of PCA |
D: HW3 |
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| 18-Oct |
Lecture 12: Visualization |
Lecture 13: Ethics |
Lab 7: Visualization |
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R:HW4 |
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| 25-Oct |
Lecture 14: Logistic Regression 1 |
Lecture 15: Logistic Regression 2 |
Lab 8: Classification |
Advanced Section 4: GLM |
R:HW5 - D:HW4 |
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| 1-Nov |
Lecture 16: Decision Tree |
Lecture 17: Bagging |
Lab 9: Decision Trees |
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| 8-Nov |
Lecture 18: Random Forest |
Lecture 19: Boosting |
Lab 10: Random Forest |
Advanced Section 5: Stacking & Mixture of Experts |
R:HW6 - D:HW5 |
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| 15-Nov |
Lecture 20: Model Interpretability |
Lecture 21: Experimental Design |
Lab 11: Model Interpretability & Ethics |
Advanced Section 6: Bandits (tentative) |
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| 22-Nov |
Lecture 22: NLP 1 |
No Lecture |
No Lab |
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R:HW7 - D:HW6 |
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| 29-Nov |
Lecture 23: NLP 2 |
Lecture 24: Final Review |
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D:HW7 |
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| 6-Dec |
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Project Submission Deadline |
Reading Period |
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| 13-Dec |
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Finals Week |
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| 20-Dec |
Projects: Final Showcase |
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