CSCI 3320: Fundamentals of Machine Learning — Spring 2023



Tuesday, 8:30 - 10:15 am, William M W Mong Eng Bldg LT Wednesday, 8:30 - 9:15 am, Science Centre L1
Wednesday, 9:30 - 10:15 am, Science Centre L1
Prof. Liwei WANG
Office Hour
Wednesday 3 - 5 pm. Please make an appointment with Prof. Wang at least half day in advance. Meeting location would be Rm 1017, SHB.
Teaching Assistant
Qinze YU Ziyuan HU Runsong ZHU Yongfeng HUANG
All updates will be sent to your emails. Please check your university emails.
Assignments (40%) + Final Examination (60%)
Book References
  • Learning from Data, by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin
  • Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy
  • Pattern Recognition and Machine Learning, by Christopher M. Bishop
  • Abundant resources available on the web
  • Tutorial sessions would be the fastest and clearest way to discuss any problem. You are also welcomed to send emails to the instructor and the TAs.


    Fundamentals of Machine Learning covers important concepts and algorithms in Machine Learning, including supervised learning, regularization and model selection, bayesian and generative learning models, kernel methods, decision trees and boosting, unsupervised learning, active learning, etc. This course will also have two lectures discussing how machine learning can be used in various applications of computer vision and natural language processing.


    All related lecture materials will be posted on Blackboard → Lecture Notes.

    Note: Lecture slides cannot be distributed outside the class.

    The following schedule (tentative) are subject to minor modifications.

    Week Date Topic Other
    1 Jan 10, 11 Introduction to Machine Learning
    2 Jan 17, 18 Why Machine Learning is Feasible?
    3 Jan 31, Feb 1 Training versus Testing - Theory of Generalization Release of Assignment 1 on Saturday evening
    4 Feb 7, 8 Training versus Testing - VC Dimension
    5 Feb 14, 15 Training versus Testing - Bias Variance Tradeoff Release of Assignment 2 on Saturday evening
    Submission of Assignment 1 before Sunday 11:59 pm
    6 Feb 21, 22 Linear Models
    7 Feb 28, Mar 1 Linear Models; Neural Networks Submission of Assignment 2 before Sunday 11:59 pm
    - Mar 7, 8 Reading Week
    8 Mar 14, 15 SGD, Overfitting and Regularization Release of Assignment 3 on Tuesday evening
    9 Mar 21, 22 Bayesian Learning Release of Assignment 4 on Saturday evening
    Submission of Assignment 3 before Sunday 11:59 pm
    10 Mar 28, 29 Deep Learning Classifiers -Part 1
    11 Apr 4 Deep Learning Classifiers -Part 2 (Continue) Release of Assignment 5 on Saturday evening
    Submission of Assignment 4 before Sunday 11:59 pm
    - Apr 5 Ching Ming Festival
    12 Apr 11, 12 Bagging and Boosting
    Unsupervised Learning
    Release of Assignment 6 on Saturday evening
    Submission of Assignment 5 before Sunday 11:59 pm
    13 Apr 18, 19 Gaussian Mixture Models, EM Algorithm, & Active Learning Submission of Assignment 6 before April 30 11:59 pm

    Assignments (40%)

    There will be six assignments in total.

    Unless further notice, all written answers for the question sets should be submitted in a single PDF file. If there is coding involved, please also compress the related codes into a ZIP file and submit it along with your PDF answer. The PDF file should be accepted by VeriGuide before submission. All the submissions should be made via Blackboard by 11:59 pm.

    Final Examination (60%)

    The final exam is on **May 6th (Saturday), 9:30-11:30 am**.

    Venue: Multi-purpose Hall, Pommerenke Student Centre.

    More information about the written Final Examination will be released later.

    Course Policy