<aside> ⚠️
Subject to change.
</aside>
| No. | Date | Lectures | 📒 📹 | Reading |
|---|---|---|---|---|
| 01 | Mar 04 | Introduction – Course Overview, Statistical Decision Theory | ✏️ ▶️ | |
| 02 | Mar 09 | Least Squares 1 – Least Squares Method. Projection Perspective. | ✏️ ▶️ | ESL 3.2 |
| 03 | Mar 11 | Least Squares 2 – Orthogonal Matrix. SVD perspective of LS. | ✏️ ▶️ | ESL 3.2 |
| 04 | Mar 16 | Least Squares 3 – First and Second moments of $\hat\beta$, $\hat{y}$, RSS. | ✏️ ▶️ | ESL 3.2 |
| 05 | Mar 18 | Inference 1 – Gauss-Markov thm. Distributions of $\hat\beta$, $\hat{y}$, RSS. | ✏️ ▶️ | ESL 3.2 |
| 06 | Mar 23 | **Inference 2** – Linear Contrasts, Multiple Contrasts, full/sub models | ✏️ ▶️ | |
| 07 | Mar 25 | Inference 3 – ANOVA, two-way ANOVA, Generalized LS | ✏️ ▶️ | |
| 08 | Mar 30 | Model Selection 1 – Bias Variance Tradeoff, Ridge regression | ✏️ ▶️ | ESL 3.4.1, 7.3 |
| 09 | Apr 01 | Model Selection 2 – Cross Validation | ✏️ | ESL 7.10 |
| 10 | Apr 06 | Model Selection 3 – Leave One Out Cross Validation, Lasso | ✏️ ▶️ | ESL 3.3, 3.4.2-3 |
| 11 | Apr 08 | Dimension Reduction 1 – Principal Component Regression | ✏️ ▶️ | ESL 3.5.1 |
| 12 | Apr 13 | Dimension Reduction 2 – Partial Least Squares | ✏️ ▶️ | ESL 3.5.2 |
| Apr 15 | Practice Session | |||
| Apr 22 | Midterm exam 9:00 am - 11:30 am | |||
| 13 | Apr 27 | Generalized Linear Model 1 – One-Parameter Exponential Family | ✏️ ▶️ | |
| 14 | Apr 29 | Generalized Linear Model 2 – Dispersion, MLE | ✏️ ▶️ | |
| 15 | May 4 | Generalized Linear Model 3 – Wald’s test, Score test | ✏️ ▶️ | |
| 16 | May 6 | Generalized Linear Model 4 – Likelihood ratio test | ✏️ ▶️ | |
| 17 | May 11 | Basis Expansion 1 – Kernel Ridge Regression, Gaussian Process Regression | ✏️ 💻 ▶️ | |
| 18 | May 13 | Basis Expansion 2 – RKHS, Kernelized GLM, Random Features | ✏️ 💻 ▶️ | |
| 19 | May 18 | Basis Expansion 3 – Polynomial Regression, Splines | ✏️ 💻 ▶️ | ESL 5.2, 5.4 |
| 20 | May 20 | Basis Expansion 4 – Smoothing Splines | ✏️ 💻 ▶️ | |
| 21 | May 27 | Special Topic 1 – Neural Network | ✏️ 💻 ▶️ | |
| 22 | Jun 1 | Special Topic 2 – Conformal Prediction | ✏️ ▶️ | |
| 23 | Jun 8 | Special Topic 3 – Regression Tree, Bagging, Boosting | 💻 ▶️ | |
| Jun 10 | Review | |||
| Jun 17 | Final exam 9:00 am - 11:45 am |