MSDM 5054: Statistical Machine Learning

HKUST

MSDM 5054: Statistical Machine Learning
Fall 2026

Course Information

Synopsis

This course covers several topics in statistical machine learning:

1. supervised learning (linear and nonlinear models, e.g. trees, support vector machines, deep neural networks),

2. unsupervised learning (dimensionality reduction, cluster trees, generative models),

3. reinforcement learning (markov decision process, deep rl).

Prerequisite: Some preliminary course on (statistical) machine learning, applied statistics, and deep learning will be helpful.

Instructors:

Time and Place:

Tu 6:30-9:20pm, Lecture Theater D (LTD), HKUST

Reference (参考教材)

An Introduction to Statistical Learning, with applications in R (ISLR). By James, Witten, Hastie, and Tibshirani

ISLR-python, By Jordi Warmenhoven.

ISLR-Python: Labs and Applied, by Matt Caudill.

Manning: Deep Learning with Python, by Francois Chollet [GitHub source in Python 3.6 and Keras 2.0.8]

MIT: Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Tutorials: preparation for beginners

Python-Numpy Tutorials by Justin Johnson

scikit-learn Tutorials: An Introduction of Machine Learning in Python

Jupyter Notebook Tutorials

PyTorch Tutorials

Deep Learning: Do-it-yourself with PyTorch, A course at ENS

Tensorflow Tutorials

MXNet Tutorials

Theano Tutorials

The Elements of Statistical Learning (ESL). 2nd Ed. By Hastie, Tibshirani, and Friedman

statlearning-notebooks, by Sujit Pal, Python implementations of the R labs for the StatLearning: Statistical Learning online course from Stanford taught by Profs Trevor Hastie and Rob Tibshirani.

Homework and Projects:

TBA (To Be Announced)

Teaching Assistant:

Email: Mr. WANG, He < aifin.hkust (add "AT gmail DOT com" afterwards) >

Schedule

Date	Topic	Instructor	Scriber
09/07/2026, Mon	Lecture 01: A Historic Overview of AI and Statistical Machine Learning. [ slides (pdf) ]	Y.Y.
09/14/2026, Mon	Lecture 02: Supervised Learning: linear regression and classification [ slides ] [ Reference ] To view .ipynb files below, you may try [ Jupyter NBViewer] Linear Regression Python Notebook [ MAFS6010_Regression.ipynb ] Linear Classification Python Notebook [ MAFS6010_Classification.ipynb ]	Y.Y.