MATH3320 - Foundation of Data Analytics - 2025/26
Course Name:
Teacher:
Course Year:
2025/26
Term:
1
Announcement
- There is no tutorial in the first week.
- HW1 has been posted. (Due date: 28 September 2025 (Sun), 11:59PM)
- (20250924) As the Hurricane Signal No. 10 is in effect, the tutorial and lecture this afternoon are cancelled. Stay safe!
- (20250924) The Tutorial 3 learning materials prepared by TA Oscar have been uploaded (see also the tutorial video on Blackboard -> Panopto).
- HW2 has been posted. (Due date: 12 October 2025 (Sun), 11:59PM)
- HW3 has been posted. (Due date: 2 November 2025 (Sun), 11:59PM)
- HW4 has been posted. (Due date: 16 November 2025 (Sun), 11:59PM)
- HW5 has been posted. (Due date: 30 November 2025 (Sun), 11:59PM)
General Information
Lecturer
-
Prof. Gary Pui Tung CHOI
- Office: LSB 204
- Email:
- Office Hours: By appointment
Teaching Assistant
-
Mr. Oscar Yau Lam CHAU
- Office: LSB 222B
- Email:
- Office Hours: Tue 12:15pm-2:15pm, Wed 10:30am-1:30pm, Fri 1:30pm-4:30pm
-
Mr. Liguang HOU
- Office: LSB 222B
- Email:
- Office Hours: Mon 9am-12pm, Wed 9am-11am, Thu 9am-12pm
Time and Venue
- Lecture: Tue 10:30am-12:15pm, NAH 213; Wed 3:30pm-4:15pm, LSK LT3
- Tutorial: Wed 2:30pm-3:15pm, LSK LT3
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 theories and methods that make up the backbone of modern mathematical data sciences, such as knowledge discovery in databases, machine learning, and mathematical artificial intelligence. Topics include regression, dimensionality reduction, clustering, classification, proper orthogonal decomposition methods, optimization, theories of nonlinear neural network and approximations. Students taking this course are expected to have knowledge of basic linear algebra.
Advisory: MATH Majors should select not more than 5 MATH courses in a term.
Textbooks
- Jeff M. Phillips, "Mathematical Foundations for Data Analysis", Springer, 2021. Online version: https://mathfordata.github.io/.
- Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, "Mathematics for Machine Learning", Cambridge University Press, 2020. Online version: https://mml-book.github.io/.
References
- Seongjai Kim, "Programming Basics and AI with Matlab and Python", 2024. Online version: https://skim.math.msstate.edu/LectureNotes/Programming-Basics-and-AI-Lecture.pdf
Pre-class Notes
Lecture Notes
- Course Information
- Review on Linear Algebra
- (20250902) Lec 01: Introduction to Regression
- (20250903) Lec 02: More on Regression
- (20250909) Lec 03: Overfitting, Cross Validation, and Regularized Regression
- (20250910) Lec 04: More on Regularized Regression and Matching Pursuit
- (20250916) Lec 05: Dimensionality Reduction
- (Update on 16/9) Note: There was a missing square term in the weighted normal euqation in Lec 02 and Lec 03 slides. Please see the updated PDF files.
- (20250917) Lec 06: More on SVD and PCA, and Introduction to Multidimensional Scaling
- (20250923) Lec 07: Multidimensional Scaling and Kernel PCA
- (20250930) Lec 08: Linear Discriminant Analysis, Matrix Completion, and Introduction to Clustering
- (20251008) Lec 09: Theoretical Aspects of Clustering Algorithms and Soft Clustering
- Note: The Midterm Exam will cover up to Lec09. Please see the latest version of the files above.
- (20251014) Lec 10: Hierarchical Clustering, Density-based Clustering, and Mean-shift Clustering (further revised on 15/10, updated some terminologies and setups in the example on page 27)
- (20251015) Lec 11: Introduction to Classification and Linear Classifiers (revised)
- (20251022) Lec 12: Logistic Regression and Support Vector Machine (revised)
- (20251028) Lec 13: More on Logistic Regression and SVM, k-NN Classifier, Decision Tree, and Introduction to Neural Network (revised)
- (20251104) Lec 14: More on Neural Networks and Introduction to Graph-Structured Data Analysis (revised)
- (20251105) Lec 15: Graph Laplacian and Spectral Properties of Graphs (revised and added more explanations)
- (20251111) Lec 16: More on Spectral Properties of Graphs, Communities in Graphs, Laplacian-based Embedding, Spectral Clustering, and Markov Processes (revised, fixed some typos and added more explanations)
- (20251112) Lec 17: PageRank (revised, fixed some typos)
- (20251118) Lec 18: Random graphs and networks (revised)
Tutorial Notes
- Tutorial 1 MATLAB code
- Tutorial 1 MATLAB code (ans)
- Tutorial 1 Python code (Google Drive link)
- Tutorial 1 Python code (ans) (Google Drive link)
- Tutorial 2 MATLAB code
- Tutorial 2 Python code (Google Drive link)
- Tutorial 2 Lecture notes
- Tutorial 3 Python code (Google Drive link)
- Tutorial 3 MATLAB code (main)
- Tutorial 3 MATLAB code (draw circle)
- Tutorial 3 MATLAB code (Kernel PCA)
- Tutorial 3 video is available on Blackboard (Blackboard -> Panopto -> Tutorial 3).
- Tutorial 4 Lecture notes
- Tutorial 5 Lecture notes
- Tutorial 6 Python code (Google Drive link)
- Tutorial 6 MATLAB code (main)
- Tutorial 6 MATLAB code (mean shift)
- Tutorial 6 Supplementary notes (Ward's Method)
- Tutorial 7 Python code (Google Drive link)
- Tutorial 7 MATLAB code
- Tutorial 8 Lecture notes
- Tutorial 8 Python code (Google Drive link)
- Tutorial 8 MATLAB code
Assignments
- HW1 (due date: 28 September 2025 (Sun) 11:59PM)
- HW2 (due date: 12 October 2025 (Sun) 11:59PM)
- HW3 (due date: 2 November 2025 (Sun) 11:59PM)
- Note: For HW3 Q4 and Q5, please download the data files via the provided Google Drive links. If you are unable to access them, you may also download them from Blackboard.
- HW4 (due date: 16 November 2025 (Sun) 11:59PM)
- Note: For HW4 Q4 and Q5, please download the data files via the provided Google Drive links. If you are unable to access them, you may also download them from Blackboard.
- HW5 (due date: 30 November 2025 (Sun) 11:59PM)
- Note: For HW5 Q5, please download the data files via the provided Google Drive links. If you are unable to access them, you may also download them from Blackboard.
Solutions
Assessment Scheme
| Homework Assignments | 30% | |
| Midterm Exam (October 21, in-class) | 30% | |
| Final Exam (Centralized, December 11 Thursday 18:30-20:30, Multi-purpose Hall, Pommerenke Student Centre) | 40% |
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:
http://www.cuhk.edu.hk/policy/academichonesty/and thereby help avoid any practice that would not be acceptable.
Assessment Policy Last updated: November 18, 2025 13:46:44