Machine Learning
CS-GY 6923, Spring 2025 at NYU
Lectures: Fridays at 11:00am-01:30pm
Location: 2 MetroTech Center Room 817, Brooklyn Campus
Instructor
Teaching Assistants
How to get started:
- Read the syllabus.
- Join our Ed Discussion message board and Gradescope with the email invitations you received earlier this week. If you didn’t receive an email, you can use the access code G3NXW3 for Gradescope. For Ed Discussion, if you didn’t receive an email, please let me know.
Week 1
- Jan 24
- LEC 01 Introduction, Loss Functions, Simple Linear Regression
- PDF
- Marked-PDF
- Demo 01 Numpy, arrays, and plotting
- Link
- Demo 02 Simple regression example
- Link
- Jan 25
- Lab 01 Release
- Link
- Due: Feb 05, 11:59pm
- Reading
- Probability Review
- Notes by A. Maleki and T. Do
- Khan Academy
- Linear Algebra Review
- Note
- Computing Gradient
- Notes by Chris Musco
- Raw Markdown
- Additional Reading
- Ch. 3.1, 3.2 in An Introduction to Statistical Learning
Week 2
- Jan 31
- LEC 02 Multiple Linear Regression, Polynimial Regression, Model Selection
- PDF
- Marked-PDF
- Demo 03 Multiple Regression
- Link
- Demo 04 Polynimial Regression, Model Order Selection
- Link
- Feb 02
- Lab 02 Release
- Link
- Due: Feb12, 11:59pm
- Reading
- Computing Gradient
- Notes by Chris Musco
- Raw Markdown
Week 3
- Feb 07
- Due: Feb 19, 11:59pm
- Feb 08
- LEC 03 Regularization, Naive Bayes
- PDF
- Marked-PDF
- Reading
- Regularization
- Caltech Lec 12
- Chapter 6.2 in AISL
- Naive Bayes
- Additional Lecture Notes
Week 4
- Feb 13
- Lab 03 Release
- Link
- Due: Feb 26, 11:59pm
- Feb 14
- LEC 04 Bayesian ML, Modeling Language
- PDF
- Marked-PDF
- Reading
- Bayes regression
- Note on Least Squares Regression from a statistical perspective
Week 5
Week 6
- Feb 28
- LEC 06 Gradient Descent
- PDF
- Marked-PDF
Week 7
Week 8
- Mar 14
- LEC 08 Federated Learning
- PDF
- Marked-PDF