Instructor Lingpeng Kong (lpk AT
Season Spring 2023
Location Tuesdays 12:30pm - 1:30pm & Fridays 12:30pm - 2:30pm @ MB167
Chang Ma, Xie Zhang, Zhiheng Lyu
Textbook Probabilistic Machine Learning: An Introduction
Tutorial TBD
Course description:

This course introduces algorithms, tools, practices, and applications of machine learning. Topics include core methods such as supervised learning (classification and regression), unsupervised learning (clustering, principal component analysis), Bayesian estimation, neural networks; common practices in data pre-processing, hyper-parameter tuning, and model evaluation; tools/libraries/APIs such as scikit-learn, Theano/Keras, and multi/many-core CPU/GPU programming.


MATH1853 or MATH2101; and COMP2119 or ELEC2543 or FITE2020


50% continuous assessment, 50% examination


Lecture     Topic/papers Recommended reading Materials
Part I: Basics     
1 Introduction [slides] [PML Ch. 1]
2 Foundations [slides] [PML Ch. 2.1.1 - 2.4.1 - 2.5.2]
3 Logistic and Linear Regression [slides] [PML Ch. 10.1 - 10.3] [PML Ch. 11.1 - 11.4] [hw1] [temp]
4 Linear Discriminant Analysis, Naive Bayes Classifiers [slides] [PML Ch. 9.1 - 9.3]
5 Neural Networks [slides] [PML Ch. 13.1 - 13.5] [PML Ch. 15.1 - 15.3]
6 Supervised Learning (Review) [slides] [MLAPP Reading Recommendation] [Tutorial 1]
7 Nonparametric Models (KNN and KDE) [slides] [PML Ch. 16]
8 Clustering / Unsupervised Representation Learning [slides] [PML Ch. 21.1 - 21.4] [PML Ch. 15.7]
9 Transformers [slides] [Baahdanau et al, 2015] [Vaswani et al, 2017] [The Annotated Transformer]
10 Reinforcement Learning and AlphaGo [slides] [Sutton and Barto, 2014] [Silver et al, 2016]



We will review your work individually to ensure that you receive due credit for your work. Please note that both your project output and logic will be considered for marking.

Policy and honor code:

You are free to discuss ideas and implementation details with other students. However, copying others’ codes will not help your study but jeopardize it. We will check your work against other submissions and Internet sources. It is easy to know if you did your own work or not. To be clear, we encourage you to discuss with your classmates but you MUST do your work independently and CANNOT simply copy. If plagiarism is identified, one may face serious consequences according to the Faculty and University policy.


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