291K Machine Learning (Fall 2022)

Course Description

Machine Learning is about developing systems that automatically improve their performance through experience. It has found massive applications in real products. Examples include systems that recommend online videos, automatic translating languages, and autonomous driving vehicles. This advanced course gives a general and in-depth introduction to the theory, models, and practical algorithms for machine learning. Topics include learning problems (supervised learning, unsupervised learning, self-supervised learning, reinforcement learning, and online learning); modeling tools (graphical models, neural networks, kernel methods, tree methods); as well as theoretical foundations (learning theory, optimization). We focus on both the principles, analytical skills and implementation practice. This course is suitable for graduate students who want to pursue a career in AI/ML, to conduct research in this area, or apply ML methods in their own projects. No prior knowledge of machine learning is assumed.

Instructor

Lei Li  (Office Hour: HFH2121, Thursdays 2-3pm, book a slot here)

Yu-Xiang Wang (Office Hour: HH 2013, Tuesdays 1-2 pm, or by appointment.)

Teaching Assistant

Time and Location

Tue/Thur 11am - 12:50pm (PHELP 3526)

Reference Textbook

The textbook below is a great resource for those hoping to brush up on the prerequisite mathematics background for this course.

Prerequisites

Linear algebra (MATH 3B), Vector Calculus (6A), Probability and Statistics (PSTAT 120A, 120B), algorithms (CS 130A & 130B), and familiarity with Python programming.

Homework Submission & Grading

Discussion Forum

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Policy

Please read the following Link carefully!

Syllabus

#
Date
Topic
Reading
Slides Homework
1
9/22
Introduction, Spam filter
PPML 1.1, PRML 4.1.1, 4.1.2
lec1
HW0, data
2
9/27
Supervised Learning, Loss function, Model Selection
PRML 1.3, 3.1, 3.2

HW0 due, HW1 out
3
9/29
Unsupervised Learning, dimensionality reduction
PRML 9.1, 12.1


4
10/4
Optimization basic: Gradient Descent and SGD
MML 7.1, lecture notes


5
10/6
Feedforward Neural Networks D2L 5


6
10/11
Convolutional Neural Networks D2L 7 & 8
HW1 due, HW2
7
10/13
Sequence Modeling and Recurrent Neural Networks D2L 9 & 10

8
10/18
Attention Mechanism and Transformers D2L 11, 15.8, 15.9, 15.10

9
10/20
Graphical Models and MLE PRML 8.1, 8.2

10
10/25
Gaussian Mixture Models, EM,LDS PRML 9, 12.2, 13.3

11
10/27
Undirected Graphical Models, Conditional Random Fields PRML 8.3, 8.4
HW2 due, HW3
12
11/1
Deep Latent Model and Approximate Inference
PRML 10, VAE, Sentence VAE

13
11/3
Sampling Methods
PRML 11

14
11/8
Convex Optimization
FML Appendix B


15
11/10
Support Vector Machine, Kernel Methods
FML 5.1 - 5.3


16
11/15
Online Learning FML 8.1,8.2

HW3 due
17
11/17
Statistical Learning Theory I
FML 2.1-2.3, 3.1


18
11/22
Statistical Learning Theory II FML 3.1, FML 11.2


19
11/29
Theory of Deep Learning and Overparameterization TBA


20
12/1
Reinforcement Learning FML 17.1-17.3




Final project poster presentation