NYU CS-GY 6923 Machine Learning
Professor Pavel Izmailov
Friday 2:00-4:30pm, 6 Metrotech, Room 315
Virtual lectures: Zoom.
Course Description
This course provides a graduate-level introduction to machine learning through a mixture of hands-on exercises and theoretical foundations. We will cover fundamentals of machine learning: regression, classification, linear models, neural networks, numerical optimization methods (gradient descent, backpropagation), unsupervised learning, and a number of other topics. We will also cover basics of language modeling and understand how systems such as ChatGPT operate. The course includes hands-on exercises with machine learning methods and covers a broad range of applications.
Professor Contact
Email: pi390@nyu.edu
Office Hours: 5:30pm-7:00pm on Mondays on Zoom
Course Assistants
Majid Daliri: daliri.majid@nyu.edu
Harsha Mupparaju: sm12754@nyu.edu
Shubham Rastogi: sr7421@nyu.edu
Office Hours: TBD
Links
Lectures
| Date | Topic | Optional Reading | Homework |
|---|---|---|---|
|
09/05/2025 Slides Recording |
• Introduction to Machine Learning • Course logistics • Supervised learning • Regression and classification • Linear regression |
• Probability review • Linear algebra review • Deep learning book, part I • The Matrix Cookbook • Probabilistic Machine Learning: An Introduction, chapter 7.8 covers matrix calculus • 2024 CS-GY 6923 lecture 1 slides by prof. Chris Musco |
• Numpy demo: demo_numpy.ipynb (not turned in) • Simple linear regression demo: demo_auto_mpg.ipynb (not turned in) • lab1.ipynb, due 11:59pm, Monday 9/15 |
|
09/12/2025 Lecutre by Majid Daliri Slides by prof. Musco |
• Data encoding • Generalized linear models • Model selection • Generalization error • Statistical learning model |
• Probabilistic Machine Learning: An Introduction, chapters 4.5.4-4.5.7 • ISLP: Cross-Validation and the Bootstrap • 2024 CS-GY 6923 lecture 2 slides by prof. Chris Musco |
• Demo 3: demo_diabetes.ipynb (not turned in) • Demo 4: demo_polyfit.ipynb (not turned in) • Lab 2: lab2.ipynb, due 11:59pm, Monday 9/22 • Complete written Homework 1, due 11:59pm, Monday 9/29. 10% bonus if you typeset solutions in Markdown or Latex! |
Acknowledgement
This class (lectures, demos and course materials) is based on the previous iteration of CS-GY 6923 taught by professor Christopher Musco.