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

Adith Santosh

at6115[at]nyu.edu

Sreeharsh Namani

sn4165[at]nyu.edu

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
HW 01 posted
PDF   
Solution   
LaTeX   
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

Feb 20
HW 02 posted
PDF   
Solution   
LaTeX   
Due: March 05, 11:59pm
Feb 21
LEC 05 Logistic Regression, Optimization
PDF   
Marked-PDF
Demo 05 Logistic regression
Link   
Reading
Logistic regression
Chapter 4.1-4.3 in AISL     
Note on Logistic Regression

Week 6

Feb 28
LEC 06 Gradient Descent
PDF   
Marked-PDF

Week 7

Mar 04
HW 03 posted
PDF   
Solution   
LaTeX   
Due: March 12, 11:59pm
Mar 07
LEC 07 Finishing SGD, Learning Theory
PDF   
Marked-PDF
Mar 08
Lab 04 Release
Link   
Due: March 23, 11:59pm
Reading
PAC Learning
Note from Nika Haghtalab
VC dimension
Note from Nika’s course
More on VC dimention and Rademacher complexity
Book Chapters 6 and 26

Week 8

Mar 14
Midterm 01
Sample   
Solution  
info
LEC 08 Federated Learning
PDF   
Marked-PDF

Week 9

Mar 21
LEC 09 Kernel Methods, Support Vector Machine
PDF   
Marked-PDF
Demo 06 SVM for MNIST Digit Recognition
Link   
Mar 22
Lab 05 Release
Link   
Due: April 09, 11:59pm
HW 04 posted
PDF   
Solution   
LaTeX   
Due: April 16, 11:59pm
Reading
SVM’s additional reading
Chapter 9 in An Introduction to Statistical Learning.

Week 10

March 28 Spring Break

Week 11

Apr 04
LEC 10 Neural Networks and Backpropagation
PDF   
Marked-PDF
Demo 07 Keras NN on synthetic data
Link   
Demo 08 Keras NN on MNIST data
Link   
Apr 04
Reading
Tensorflow playground

Week 12

Apr 11
LEC 11 Convolution Neural Networks
PDF   
Marked-PDF
Demo 09 CNN using Keras
Link   
Demo 10 Train a CNN for CIFAR-10
Link    To make sure Colab is using a GPU, click on the Runtime tab and then Change Runtime Environment. Select GPU under hardware acceleration.

Week 13

Apr 18
LEC 12 Finishing CNN, Transfer Learning, Autoencoders
PDF   
Marked-PDF
Demo 11 Demo on adversarial examples
Link   

Week 14

Apr 21
HW 05 posted
PDF   
Solution   
LaTeX   
Due: May 03, 11:59pm
UScities.txt dataset
Apr 25
LEC 13 PCA, Semantic Embeddings
PDF   
Marked-PDF