Machine Learning
CS-GY 6923, Fall 2024 at NYU
Lectures: Fridays at 2:00-4:30pm
Location: Jacobs Academic Bldg, Room 473, 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 access code Z3GWBN for Gradescope (the link above for Ed Discussion will automatically let you join the class without a code).
Week 1
- Sep 06
- LEC 01 Introduction, Loss Functions, Simple Linear Regression
- PDF
- Marked-PDF
- Demo 01 Numpy, arrays, and plotting
- Link
- Demo 02 Simple regression example
- Link
- Sep 07
- Lab 01 Release
- Link
- Due: Sep16, 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
- Sep 13
- 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
- Sep 14
- Lab 02 Release
- Link
- Due: Sep24, 11:59pm
- Reading
- Computing Gradient
- Notes by Chris Musco
- Raw Markdown
- Regularization
- Caltech Lecture 12
Week 3
- Sep 18
- Due: Oct 01, 11:59pm
- Sep 20
- LEC 03 Naive Bayes
- PDF
- Marked-PDF
- Reading
- Regularization
- Caltech Lec 12
- Chapter 6.2 in AISL
- Naive Bayes
- Additional Lecture Notes
Week 4
- Sep 27
- LEC 04 Bayesian Machine Learning, Modeling Language
- PDF
- Marked-PDF
- Sep 28
- Lab 03 Release
- Link
- Due: Oct 08, 11:59pm
- Reading
- Bayes regression
- Note on Least Squares Regression from a statistical perspective
Week 5
Week 6
- Oct 11
- LEC 06 (Stochastic) Gradient Descent
- PDF
- Marked-PDF
Week 7
Week 8
Week 9
- Nov 01
- LEC 08 Kernel Methods, Support Vector Machines
- PDF
- Marked-PDF
- Demo 06 SVM for MNIST Digit Recognition
- Link
- Reading
- SVM’s additional reading
- Chapter 9 in An Introduction to Statistical Learning.
Week 10
- Nov 07
- Lab 05 Release
- Link
- Due: Nov 19, 11:59pm
- Due: Dec 02, 11:59pm
- Nov 08
- LEC 09 Neural Networks and Backpropagation
- PDF
- Marked-PDF
- Demo 07 Keras NN on synthetic data
- Link
- Demo 08 Keras NN on MNIST data
- Link
- Nov 09
- Reading
- Tensorflow playground
Week 11
- Nov 15
- LEC 10 Convolution, Feature Extraction, Adversarial Examples
- PDF
- Marked-PDF
- Demo 08 CNN using Keras
- Link
- Demo 09 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 12
- Nov 22
- LEC 11 Auto-encoders, Principal Component Analysis,
- PDF
- Marked-PDF
Week 13
Week 14
- Dec 06
- LEC 12 Semantic embeddings, Image Synthesis
- PDF
- Marked-PDF
Week 15
- Dec 11
- LEC 13 Self-supervised learning, what’s next?
- PDF
- Marked-PDF
- Dec 13
- Reading Day
Finals Week
- Dec 20
- Final Exam
- Location: TBD
Time: TBD