lectures
- Lecture 1: Introduction to CS109A (Sep. 01, 2021)
- Lecture 2: Introduction to PANDAS and EDA (Sep. 08, 2021)
- Lecture 3: Introduction to Regression kNN and Linear Regression (Sep. 13, 2021)
- Lecture 4: Multi-linear and Polynomial Regression (Sep. 15, 2021)
- Lecture 5: Model Selection and Cross Validation (Sep. 20, 2021)
- Lecture 6: Regularization Ridge and Lasso Regression (Sep. 22, 2021)
- Lecture 7: Probability (Sep. 27, 2021)
- Lecture 8: Inference in Regression and Hypothesis Testing (Sep. 29, 2021)
- Lecture 9: Missing Data & Imputation (Oct. 04, 2021)
- Lecture 10: Principal Component Analysis (Oct. 06, 2021)
- Lecture 12: Visualization (Oct. 18, 2021)
- Lecture 13: EthiCS (Oct. 20, 2021)
- Lecture 14: Logistic Regression I (Oct. 25, 2021)
- Lecture 15: Logistic Regression II (Oct. 27, 2021)
- Lecture 16: Decision Trees (Nov. 01, 2021)
- Lecture 17: Bagging (Nov. 03, 2021)
- Lecture 18: Random Forest (Nov. 08, 2021)
- Lecture 19: Random Forrest II (Nov. 10, 2021)
- Lecture 20: Boosting, Gradient Boosting (Nov. 15, 2021)
- Lecture 21: AdaBoost (Nov. 17, 2021)
- Lecture 22: Working Example (Nov. 22, 2021)
- Lecture 23: Natural Language Processing (Nov. 29, 2021)
- Lecture 24: Review (Dec. 01, 2021)