Schedule
The table below maps each Friday to the lecture topic and materials. Click a topic to open the lecture page with slides, code, readings, and lab.
| Date | Topic | Reading | Materials | Labs | Extras | Assignment |
|---|---|---|---|---|---|---|
| Aug 28 |
Introduction — supervised vs. unsupervised; regression, classification, clustering, dimensionality reduction Quiz — Rel: Aug 28, 11:00 AM. Due: Sep 4, 8:59 AM. |
ISLR ch. 2 | slides | — | — | — |
| Sep 4 |
Training models — bias-variance, training/validation/testing, resampling Quiz — Rel: Sep 4, 11:00 AM. Due: Sep 11, 8:59 AM. |
ISLR §2.2, ch. 5 | slides | Lab 2 |
bootstrap permutation test |
— |
| Sep 11 |
Regression I — subset selection, ridge, lasso Quiz — Rel: Sep 11, 11:00 AM. Due: Sep 18, 8:59 AM. |
ISLR ch. 3, 6 | slides | Lab 3 |
model selection shrinkage methods partial least squares |
HW1 Rel: Sep 11, 11:00 AM Due: Sep 25, 11:59 PM |
| Sep 18 |
Regression II — non-linear regression, splines, GAMs Quiz — Rel: Sep 18, 11:00 AM. Due: Sep 25, 8:59 AM. |
ISLR ch. 7 | slides | Lab 4 | nonlinear methods | |
| Sep 25 |
Classification I — logistic regression, generative models Quiz — Rel: Sep 25, 11:00 AM. Due: Oct 2, 8:59 AM. |
ISLR ch. 4 |
slides annotated |
Lab 5 | classification summary |
HW2 Rel: Sep 25, 11:00 AM Due: Oct 9, 11:59 PM |
| Oct 2 |
Classification II — support vector machines Quiz — Rel: Oct 2, 11:00 AM. Due: Oct 9, 8:59 AM. |
ISLR ch. 9 |
slides annotated |
Lab 6.1 (simulations) Lab 6.2 (real data) |
— | |
| Oct 9 | Fall Break — no class | — | — | — | — | — |
| Oct 16 |
Clustering I — k-means, cluster evaluation Quiz — Rel: Oct 16, 11:00 AM. Due: Oct 23, 8:59 AM. |
ISLR §10.3 |
slides annotated |
Lab 7 | — |
HW3 Rel: Oct 16, 11:00 AM Due: Oct 30, 11:59 PM |
| Oct 23 |
Clustering II — Gaussian mixture models Quiz — Rel: Oct 23, 11:00 AM. Due: Oct 30, 8:59 AM. |
PRML §9.1–9.2 |
slides annotated |
Lab 8 | — | |
| Oct 30 |
Dimensionality reduction — principal component analysis Quiz — Rel: Oct 30, 11:00 AM. Due: Nov 6, 8:59 AM. |
ISLR §6.3 |
slides annotated |
Lab 9 | — |
HW4 Rel: Oct 30, 11:00 AM Due: Nov 13, 11:59 PM |
| Nov 6 |
Decision trees — CART, regression and classification trees Quiz — Rel: Nov 6, 11:00 AM. Due: Nov 13, 8:59 AM. |
ISLR §8.1 | TBD | TBD | — | |
| Nov 13 |
Ensemble learning — bagging, random forests, boosting Quiz — Rel: Nov 13, 11:00 AM. Due: Nov 20, 8:59 AM. |
ISLR §8.2 | TBD | TBD | — |
HW5 Rel: Nov 13, 11:00 AM Due: Nov 27, 11:59 PM |
| Nov 20 |
Multiple hypothesis testing — controlling error rates across many simultaneous tests Quiz — Rel: Nov 20, 11:00 AM. Due: Nov 27, 8:59 AM. |
TBD | TBD | TBD | — | |
| Nov 27 | Thanksgiving — no class | — | — | — | — | — |
| Dec 4 | In-class final project presentations (9:00–10:50 AM) | — | — | — | — |
Project Slides due: Dec 4, 8:59 AM Report due: Dec 9, 11:59 PM |
Note
Reading codes:
ISLR — James, Witten, Hastie, Tibshirani, An Introduction to Statistical Learning (Springer, 2013).
ESL — Hastie, Tibshirani, Friedman, The Elements of Statistical Learning (Springer, 2009).
PRML — Bishop, Pattern Recognition and Machine Learning (Springer, 2006).
UDL — Prince, Understanding Deep Learning (MIT, 2024).