Date Topic Due
Module 1: Background
01/06 Lecture #1 (Bo Dai):
Course Overview
[ slides | assignment(pdf) solution ]
  • Background Test Out (See assignment)

01/08 Lecture #2 (Bo Dai):
Linear Algebra
[ slides ]
  • Background Test Due (Hand in during class)

01/13 Lecture #3 (Bo Dai):
Probabilistic and Statistics
[ slides notes reading ]

01/15 Lecture #4 (Bo Dai):
Optimization
[ slides | assignment(pdf) | assignment(tex) | solution ]
  • HW1 out

01/20 No Class (Martin Luther King Day)
01/22 Lecture #5 (Haotian Sun & Tianyi Chen):
Python / Numpy Review Session (TA session)
[ code & data | recording ]

Module 2: Supervised Learning
01/27 Lecture #6 (Bo Dai):
Linear Regression
[ slides ]

01/29 Lecture #7 (Bo Dai):
Logistic Regression
[ slides ]
  • HW1 Due

02/03 Lecture #8 (Bo Dai):
Multiclass Logistic Regression & Naive Bayes Classifier
[ slides | assignment(pdf) | assignment(tex) | solution ]
  • HW2 Out

02/05 Lecture #9 (Bo Dai):
Generative Model vs Discriminative Model
[ slides reading ]

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

02/12 Lecture #11 (Bo Dai):
Backpropagation
[ slides ]

02/17 Lecture #12 (Bo Dai):
CNNs
[ slides ]
  • HW2 Due

02/19 Lecture #13 (Bo Dai):
RNNs
[ slides ]

02/24 Lecture #14 (Zihao Zhao, Binyue Deng, Jonathan Li):
PyTorch Review Session (TA session)
[ code & data | recording ]

Module 3: Unsupervised Learning
02/26 Lecture #15 (Bo Dai):
Density Estimation: Gaussian Mixture Models
[ slides ]

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

03/05 Lecture #17 (Bo Dai):
Variational Auto-Encoder
[ slides | assignment(pdf) | assignment(tex) | solution | midterm practice ]
  • HW3 & Midterm Practice Out

03/10 Lecture #18 (Bo Dai):
Dimension Reduction & Review
[ slides ]

03/12 Lecture #19 (Bo Dai):
Midterm Exam

03/17 No Class (Spring Break)
03/19 No Class (Spring Break)
03/24 Lecture #20 (TA Teams):
Midterm Solution Review & Regrade Discussion
[ solution ]
  • HW3 Due

03/26 Lecture #21 (Bo Dai):
Representation Learning
[ slides ]

Module 4: Large Language Models
03/31 Lecture #22 (Bo Dai):
LLMs: Attention and Transformer
[ slides ]

04/02 Lecture #23 (Bo Dai):
LLMs: Instruction Fine-Tuning & RLHF
[ slides ]

Module 5: Projects
04/07 Lecture #24 (Bo Dai):
Project Presentation
  • Presenting Group No. 19, 16, 13, 17, 1
  • Zoom Link

04/09 Lecture #25 (Bo Dai):
Project Presentation
  • Presenting Group No. 12, 10, 2, 7, 11, 20
  • Zoom Link

04/14 Lecture #26 (Bo Dai):
Project Presentation
  • Presenting Group No. 8, 3, 15, 9, 23, 22
  • Zoom Link

04/16 Lecture #27 (Bo Dai):
Project Presentation
  • Presenting Group No. 6, 4, 18, 21, 14, 5
  • Zoom Link

04/21 No Class; Project Report Due