Machine Learning

COMP 5212, Fall 2024
The Hong Kong University of Science and Technology
Junxian He

Course description: this is an advanced machine learning course to provide a broad introduction to machine learning, covering foundational machine learning concepts, theory, algorithms, and applications. The topics include supervised learning (logistic regression, linear models, classification, SVM, kernal methods, naive Bayes), unsupervised learning (clustering, expectation maximization, graphical models, HMM), and reinforcement learning. It will include some optimization/learning topics (gradient descent, SGD, MLE and MAP estimation) as well. Certain aspects of deep learning are introduced（neural network basics, architectures, VAEs, GANs). The last lectures will give a general introduction to techniques in large language models.

Course Information

Course Logistics (Grading, Policy, etc.)

Time and Location

Lectures: Tue, Thu 1:30 PM - 2:50 PM at LG3008, Lift 10-12

Course Staff and Office Hours

Instructor: Junxian He. Office Hour: Tue 5 PM - 6 PM at Room 3512
TA1: Yuzhen Huang. Office Hour: Wed 9 AM - 10AM at the common place of SENG (near Lift 27/28, Room 2580)
TA2: Jinghan Zhang. Office Hour: Fri 6 PM - 7 PM at the common place of SENG (near Lift 27/28, Room 2580)

Contact and Communication

Canvas is the main platform for communication about technical questions of lecture contents and homeworks. Please do not directly email the teaching staff on technical questions which may easily get lost. The teaching staff will try to answer questions on Canvas in a prompt manner. Some rules:

• All announcements and communications will happen over Canvas
• Please utilize the Canvas Discussion page to ask questions. There are pre-existed discussion topics initiated by the staff such as lecture questions and each homework. Please post your questions in the respective thread, you are also encouraged to answer others’ questions if you know the answer.
• While we allow students to optionally post anonymous questions and answers on Canvas, please do not post anything inappropriate as anonymous users – in such cases, we will turn off the anonymous option.
• If you have non-technical, private matters, please email the teaching staff directly.
• Please consult the Course Logistics page before asking logistical questions.
• For longer discussions, please come to office hours.

Prerequisites and Materials

Students are required to be familiar with probability and linear algebra, and able to program well in Python. The CMU 10701 self-assessment exam is a good resource to roughly give you a notion of the background that is required for this course.

This course does not have a textbook, but here are some helpful materials:
Stanford CS229 Notes
Probability Review
The Matrix Cookbook
Linear Algebra Review

Lecture Schedule

The lecture schedule below is tentative and subject to change.