Course Schedule
Lecture topics are tentative and subject to change.
Class schedule
- Date
-
- Event
- Assignment
- Jan 13
-
- Lecture 0 Course Introduction
- HW0
out
- Jan 15
-
- Lecture 1 Introduction to Least Squares Regression
- HW0
due
- Jan 20
- MLK Day! No lecture
- Jan 22
- Lecture 2 Linear Algebra Review
- Jan 27
-
- Lecture 3 SVD and Regression
- Jan 29
-
- Lecture 4 Regularization in Regression
- Feb 3
-
- Lecture 5 Matrix Factorization for Recommendation
- HW1
out
- Feb 5
-
- Lecture 6 Probability Theory Review
- Feb 10
-
- Lecture 7 Introduction to Classification
- Feb 12
-
- Lecture 8 Logistic Regression
- Feb 14
-
- Deadline
- HW1
due
- Feb 17
-
- Lecture 9 Introduction to Deep learning
- Feb 19
-
- Lecture 10 Perceptron & Support Vector Machines
- Feb 24
-
- Lecture 11 Duality & Support Vector Machines
- HW2
out
- Feb 26
-
- Lecture 12 Hands on Machine Learning
- Mar 3
-
- Lecture 13 Non Linear SVMs
- Mar 5
-
- Lecture 14 Clustering
- Mar 7
-
- Deadline
- HW2
due
- Mar 10
-
- No lecture
- Mar 12
-
- Midterm
- Mar 17
- Spring Break! No lecture
- Mar 19
- Spring Break! No lecture
- Mar 24
-
- Lecture 15 Class Projects Discussion
- HW3
out
- Mar 26
- Mar 31
-
- Lecture 17 Language Modeling Part 1
- Apr 2
-
- Lecture 18 Language Modeling Part 2
- HW3
due
- Apr 7
-
- Lecture 19 Graph Clustering
- Apr 9
-
- Lecture 20 (Guest) Modern ML: Pre-training, Fine-tuning, In-context learning, PEFT (Cho-Jui Hsieh)
- HW4
out
- Apr 14
-
- Lecture 21 (Guest) Distributed ML and accelerators (Rohan Anil)
- Apr 16
-
- Lecture 22 (Guest) Diffusion Models (Chitwan Saharia)
- Apr 21
-
- Lecture 23 (Guest) Reinforcement Learning (Nived Rajaraman)
- HW4
due
- Apr 23
- Project Presentations
- Apr 28
- Project Presentations
- May 3
- Project Report Due