About the Course
See the syllabus for details. Topics covered by this course include but are not limited to:
- GLM basics: exponential families, link functions, likelihood, quasi-likelihood, conditional likelihood.
- Machine Learning: linear models, optimization, kernel methods.
- Deep Learning: MLP, CNN, RNN, Generative models.
Instructor: Yao Li
Teaching Assistant: Younghoon Kim
Class: MW 1:25PM-2:40PM, Hanes 130
Office Hours:
- Instructor Office Hours: W 3:00PM-4:00PM; by appointment; Hanes 334.
- TA Office Hours: TTH 11:00AM-12:00PM; Hanes B-4.
Course Material
| Date | Lecture | Lab |
|---|---|---|
| Jan. 9 | Welcome and Preliminary | |
| Jan. 11 | Likelihood | |
| Jan. 16 | Holiday | |
| Jan. 18 | Cancel | |
| Jan. 23 | Smoothing Methods | |
| Jan. 25 | GLM Bascis | Lab 1: Smoothing Method |
| Jan. 30 | GLM Bascis | |
| Feb. 1 | GLM Bascis | |
| Feb. 6 | MLE and Information | Lab 2: Model Selection |
| Feb. 8 | Iterated Weighted Least Squares | |
| Feb. 13 | Well-Being Day | |
| Feb. 15 | Models for Binary Data | |
| Feb. 20 | Multinomial Regression Models | |
| Feb. 22 | Models for Count Data I | Lab 3: Multinomial Regression |
| Feb. 27 | Models for Count Data II | |
| Bootstrap Inference in GLM | ||
| Mar. 1 | Constant Coefficient of Variation Models | Lab 4: Overdispersion and Zero-Inflation |
| Mar. 6 | Quasi-likelihood | |
| Mar. 8 | Mid-term Exam | |
| Mar. 13 | Spring Break | |
| Mar. 15 | Spring Break | |
| Mar. 20 | Overview of Machine Learning | Lab 5: Boostrap |
| Mar. 22 | Linear Regression and Classification | |
| Mar. 27 | Optimization in ML | Lab 6: Python and Pytorch |
| Mar. 29 | Optimization in ML | |
| Apr. 3 | Kernel Methods | |
| Apr. 5 | Multilayer Perceptron | |
| Apr. 10 | Convolutional Neural Networks | Lab 7: DNN Training |
| Apr. 12 | Recurrent Neural Networks | |
| Apr. 17 | GRU and LSTM | |
| Apr. 19 | NLP Pre-training | |
| Apr. 24 | Generative Models | |
| Apr. 26 | Adversarial Attack |
Homework Tracker
All homework assignments are to be submitted via Sakai. Late homework will receive a grade of 0.
| Date assigned | Instructions | Due Date (Time) | |
|---|---|---|---|
| Jan. 11 | HW1 | Jan. 22 (11:55 PM) | |
| Feb. 01 | HW2 | Feb. 12 (11:55 PM) | |
| Feb. 15 | HW3 | Feb. 26 (11:55 PM) | |
| Mar. 06 | HW4 | Mar. 26 (11:55 PM) | |
| Mar. 27 | HW5 | Apr. 16 (11:55 PM) | |
| Apr. 17 | HW6 | Apr. 30 (11:55 PM) |
Final Project
This course includes a take-home final project in lieu of a final exam.
Reading
- Generalized Linear Models, by P. McCullagh and J.A. Nelder.
- Extending the linear model with R, by Julian J. Faraway.
- Foundations of machine learning, by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
- Convex optimization, by Stephen Boyd, and Lieven Vandenberghe.
- Deep Learning, by Ian Goodfellow, Yoshua Bengio, Aaron Courville.
This page was last updated on 2023-05-01 16:58:49 Eastern Time.