Machine Learning

COMP 5212, Spring 2026
The Hong Kong University of Science and Technology
Junxian He, Yi R. Fung

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 4:30 PM - 5:50 PM at Room 4619 (Lift 31-32)

Course Staff and Office Hours

Instructor: Junxian He, Yi R. Fung
TA1: Kashun Shum (ksshumab@connect.ust.hk)
TA2: Junlong Li (jlini@cse.ust.hk)
TA3: Weihao Zeng (wzengak@connect.ust.hk)

Contact and Communication

Canvas is the main platform for communication about technical questions of lecture contents and homeworks.

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. Slides will be posted here when available.