Date Topic Due
Module 1: Background
01/12 Lecture #1 (Bo Dai):
Course Overview
[ slides | assignment(pdf) ]
  • Background Test Out (See assignment)

01/14 Lecture #2 (Bo Dai):
Linear Algebra
[ slides ]
  • Background Test Due (Hand in during class)

01/19 No Class (Martin Luther King Day)
01/21 Lecture #3 (Bo Dai):
Probabilistic and Statistics

01/26 Lecture #4 (Bo Dai):
Optimization
  • HW1 out

01/28 Lecture #5 (TA Teams):
Python / Numpy Review Session (TA session)

Module 2: Supervised Learning
02/02 Lecture #6 (Bo Dai):
Linear Regression

02/04 Lecture #7 (Bo Dai):
Logistic Regression
  • HW1 Due

02/09 Lecture #8 (Bo Dai):
Multiclass Logistic Regression & Naive Bayes Classifier
  • HW2 Out

02/11 Lecture #9 (Bo Dai):
Generative Model vs Discriminative Model

02/16 Lecture #10 (Bo Dai):
Neural Networks
  • Team Formation Due

02/18 Lecture #11 (Bo Dai):
Backpropagation

02/23 Lecture #12 (Bo Dai):
CNNs
  • HW2 Due

02/25 Lecture #13 (Bo Dai):
RNNs

03/02 Lecture #14 (TA Teams):
PyTorch Review Session (TA session)

Module 3: Unsupervised Learning
03/04 Lecture #15 (Bo Dai):
Density Estimation: Gaussian Mixture Models

03/09 Lecture #16 (Bo Dai):
Clustering: K-means ↔ Gaussian Mixture Models

03/11 Lecture #17 (Bo Dai):
Variational Auto-Encoder
  • HW3 Out

03/16 Lecture #18 (Bo Dai):
Dimension Reduction & Review

03/18 Lecture #19 (Bo Dai):
Midterm Exam

03/23 No Class (Spring Break)
03/25 No Class (Spring Break)
03/30 Lecture #20 (TA Teams):
Midterm Solution Review & Regrade Discussion (TA session)
  • HW3 Due

04/01 Lecture #21 (Bo Dai):
Representation Learning

Module 4: Large Language Models
04/06 Lecture #22 (Bo Dai):
LLMs: Attention and Transformer
  • Project Presentation Sign-up

04/08 Lecture #23 (Bo Dai):
LLMs: Instruction Fine-Tuning

04/13 Lecture #24 (Bo Dai):
LLMs: RLHF

Module 5: Projects
04/15 Lecture #25 (Bo Dai):
Project Presentation

04/20 Lecture #26 (Bo Dai):
Project Presentation

04/22 Lecture #27 (Bo Dai):
Project Presentation

04/27 No Class
05/04 Project Report Due