MATH3320 - Foundation of Data Analytics - 2019/20

Course Year: 


  • Course Online [Download file]
  • Midterm exam is scheduled on 12:30pm - 2:00pm, 29 Oct in AB1-G03. The exam covers all the materials taught in lectures (mainly lect 1 and lect 3:chp1-6)and tutorials.
  • Special grading arrangement: Tutorial and homework 30%, Midterm 35%, Project 35%. The project due date is revised as Dec, 17, 2019
  • The remaining materials for learning is as follows:
  • Please hand in the reports of the projects by sending emails to TAs before deadline.

General Information


  • Prof. Zeng Tieyong
    • Office: LSB225
    • Tel: 39437966
    • Email:
  • Yang Fan
    • Office: LSB222B
    • Tel: 39437963
    • Email:
  • Zhu Yumeng
    • Office: LSB222B
    • Tel: 39437963
    • Email:

Time and Venue

  • Lecture: M9:30-10:15; T12:30-14:15
  • Tutorial: M8:30-9:15, AB1-G03

Course Description

This course gives an introduction to computational data analytics, with emphasis on its mathematical foundations. The goal is to carefully develop and explore mathematical methods that build up the backbone of modern data analysis, such as machine learning, data mining and artificial intelligence. Topics include: Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction, classification, clustering, deep neural network as well as dictionary learning and basis pursuit. Students taking this course are expected to have knowledge in basic linear algebra.


  • Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, The MIT Press, 2016.
  • Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  • Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.:


  • Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014
  • Richard Duda, Peter Hart and David Stock,Pattern Classification, Wiley-Interscience, 2nd Edition, 2015.
  • Tom Mitchell, Machine Learning, 1st Edition, McGraw-Hill, 1997
  • Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. "Mathematics for Machine Learning." (2018).

Pre-class Notes

Lecture Notes

Class Notes

Tutorial Notes



Useful Links

Honesty in Academic Work

The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism. Any related offence will lead to disciplinary action including termination of studies at the University. Although cases of cheating or plagiarism are rare at the University, everyone should make himself / herself familiar with the content of the following website:

and thereby help avoid any practice that would not be acceptable.

Assessment Policy

Last updated: November 25, 2019 09:59:17