CSCI 3320: Fundamentals of Machine Learning — Spring 2022



Tuesday, 8:30 - 10:15 am, Science Centre L1 Wednesday, 8:30 - 9:15 am, William M W Mong Eng Bldg LT
Wednesday, 9:30 - 10:15 am, William M W Mong Eng Bldg LT
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
Yanyang LI Mark ZHAO Zelin ZHAO Shijia 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.

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

    Week Date Topic Other
    1 Jan 11, 12 Introduction to Machine Learning
    2 Jan 18, 19 Why Machine Learning is Feasible? - Part 1
    3 Jan 25, 26 Why Machine Learning is Feasible? - Part 2 Release of Assignment 1 on Wednesday afternoon
    4 Feb 8, 9 Bias Variance Tradeoff; Linear Models
    5 Feb 15, 16 Linear Models and Overfitting Release of Assignment 2 on Wednesday afternoon
    Submission of Assignment 1 before Thursday 11:59 pm
    6 Feb 22, 23 Regularization and Validation
    7 Mar 1, 2 MLE, MAP. and Generative Learning Algorithm Release of Assignment 3 on Wednesday afternoon
    Submission of Assignment 2 before Wednesday 11:59 pm
    8 Mar 8, 9 LDA, Naive Bayes Classifier and Fisher's LDA
    9 Mar 15, 16 Neural Networks Release of Assignment 4 on Wednesday afternoon
    Submission of Assignment 3 before Thursday 11:59 pm
    10 Mar 22, 23 Optimizing the Neural Networks and Large Margin Classifier
    11 Mar 29, 30 Largin Margin Classifier (Continue) Release of Assignment 5 on Thursday morning
    - Apr 5, 6 Reading Week and Ching Ming Festival Submission of Assignment 4 before Thursday 11:59 pm
    12 Apr 12, 13 Bagging and Boosting
    Unsupervised Learning
    Submission of Assignment 5 before Saturday 11:59 pm
    13 Apr 19, 20 Gaussian Mixture Models, EM Algorithm, & Active Learning Release of Assignment 6 on April 16
    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 4th, 9:30-11:30 am on Zoom**.

    There will be a rehearsal session on **May 1st at 9:30 am on Zoom** to simulate the key steps of the exam.

    Please refer to the announcement on Blackboard for more details.

    Course Policy