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: 5:30pm-7:00pm on Tuesdays on Zoom

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 (Andy Han): 1:30pm-3:00pm on Mondays, 370 Jay Street (in-person) or Google Meet (remote). See Ed post for updates.

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!
02/05/2026 Slides Recording	• Underfitting and overfitting • Linear equations and pseudo-inverse • Regularization • Classification and logistic regression	• Moore–Penrose inverse wiki • Stanford cs229 lecture notes, chapter 2 • Deep learning book, chapter 5.5 • The Elements of Statistical Learning, chapters 3.4, 4.4 • Probabilistic Machine Learning: An Introduction, chapter 10.1-10.2.3	• Demo on underfitting, overfitting and regularization: demo-overfitting-underfitting-regularization.ipynb (not turned in) • Demo on classification and logistic regression: demo-logistic-regression.ipynb, (not turned in)
02/12/2026 Slides Recording	• Random variables, likelihoods • Logistic regression • Multiclass logistic regression • Optimization • (Stochastic) Gradient Descent	• Probabilistic Machine Learning: An Introduction, chapter 10.1-10.2.3 • Deep learning book, chapters 4, 8-8.1.3, 8.3, 8.5 • Nocedal & Wright — Numerical Optimization (2nd ed., 2006); classic textbook on optimization • Kingma & Ba (2015) — Adam: A Method for Stochastic Optimization	• Lab 3: lab3-regularization-logreg-gd.ipynb, due 11:59pm, Monday 2/23
02/19/2026 Slides Recording	• Neural Networks • Universal Approximation • Backpropagation • Autograd	• 3Blue1Brown video 1, video 2 on neural nets • Deep learning book, chapter 6 • Neural Networks and Deep Learning by Michael Nielsen, chapter 4	• Demo on simple MLP training: demo-mlp.ipynb (not turned in) • lab4-mlp.ipynb, due 11:59pm, Monday 3/2

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.