NYU CS-GY 6923 Machine Learning (Spring 2026)
Professor Pavel Izmailov
Thursday 5:00-7:30pm, Jacobs Hall, 6 Metrotech, Room 475
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: TBD
Course Assistants
Andy Han: ah7660@nyu.edu
Hashim Zia: hz2776@nyu.edu
Harsha Mupparaju: sm12754@nyu.edu
Riddhi Sharma: rs9631@nyu.edu
Riyam Patel: rp4334@nyu.edu
Office Hours: TBD
Links
Lectures
| Date | Topic | Optional Reading | Homework |
|---|---|---|---|
|
01/22/2026 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 |
• 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 2/2 |
|
01/29/2026 Slides Recording |
• Multiple linear regression • Categorical features • Generalized linear models • Cross-validation and model selection |
• 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 2/9 • Complete written Homework 1, due 11:59pm, Monday 2/16. 10% bonus if you typeset solutions in Markdown or Latex! |
Previous Iterations
Materials for the Fall 2025 iteration of this class are available here.
Part of this class (early lectures, demos and course materials) is based on the previous iteration of CS-GY 6923 taught by professor Christopher Musco.