| Semester | Spring 2026 |
|---|---|
| Time & Days | MW 10:30AM - 11:45AM |
| Location | E2-2 1122 |
| Instructor | Kihyuk Hong (E2-2 #3104) |
| Office Hours | Thursdays 2:00PM - 3:00PM (E2-2 #3104) |
| [email protected] | |
| TA | Jinyoung Hong ([email protected]) |
| Lecture Note | Lecture Note (Last updated: March 19, 2026 11:06 PM (GMT+9) ). Scribe |
| Midterm | TBD |
| Final | TBD |
| Textbook | (optional) The Elements of Statistical Learning (public pdf) |
<aside> ⚠️
Subject to change.
</aside>
** ESL: The Elements of Statistical Learning book. LN: Lecture notes. Optional. Required*
** DP : Data project. HW : Homework
| No. | Date | Lectures | 📒 📹 | Reading* |
|---|---|---|---|---|
| 01 | Mar 04 | Introduction – Course Overview, Statistical Decision Theory | ✏️ ▶️ | ESL 2.1-5, LN 1 |
| 02 | Mar 09 | Least Squares 1 – Least Squares Method. Projection Perspective. | ✏️ ▶️ | ESL 3.2, LN 3 |
| 03 | Mar 11 | Least Squares 2 – Orthogonal Matrix. SVD perspective of LS. | ✏️ ▶️ | LN 3 |
| 04 | Mar 16 | Least Squares 3 – First and Second moments of $\hat\beta$, $\hat{y}$, RSS. | ✏️ ▶️ | LN 3 |
| 05 | Mar 18 | Inference 1 – Gauss-Markov thm. Distributions of $\hat\beta$, $\hat{y}$, RSS. | ✏️ ▶️ | |
| 06 | Mar 23 | Inference 2 – Hypothesis Testing. Linear Contrasts | ||
| 07 | Mar 25 | Inference 3 – Simultaneous Multiple Contrasts. Full vs Sub Model | ||
| 08 | Mar 30 | Inference 4 – ANOVA, two-way ANOVA, ANCOVA | ||
| 09 | Apr 01 | |||
| 10 | Apr 06 | Model Selection 1 – Bias Variance Tradeoff | ||
| 11 | Apr 08 | Model Selection 2 – Best Subset Selection, Ridge, Lasso | ||
| 12 | Apr 13 | Model Selection 3 | ||
| 13 | Apr 15 | Review | ||
| Apr 22 | Midterm exam 9:00 am - 11:45 am | |||
| 14 | Apr 27 | GLM 1 | ||
| 15 | Apr 29 | GLM 2 | ||
| 16 | May 4 | Kernel Regression | ||
| 17 | May 6 | |||
| 18 | May 11 | |||
| 19 | May 13 | |||
| 20 | May 18 | |||
| 21 | May 20 | |||
| 22 | May 27 | |||
| 23 | Jun 1 | |||
| 24 | Jun 3 | |||
| 25 | Jun 8 | |||
| 26 | Jun 10 | Review | ||
| Jun 17 | Final exam 9:00 am - 11:45 am |