MATH3320 - Foundation of Data Analytics - 2024/25

Announcement

• Final exam: 17 December 2024(Tuesday), 12:30 - 14:30, University Gymnasium

---

General Information

Lecturer

• Prof. Fenglei Fan
  • Office: LSB232A
  • Tel: 84919576
  • Email:

Teaching Assistant

• Ziyang Xu
  • Office: LSB G08
  • Tel: 3943 7978
  • Email:
• Zijun Ding
  • Office: LSB 222C
  • Tel: 3943 8570
  • Email:

Time and Venue

• Lecture: Mon 12:30 - 13:15 (Mong Man Wai Bldg 702) Tue 12:30PM - 2:15PM (Mong Man Wai Bldg 702)
• Tutorial: Tue 11:30AM - 12:15AM (Mong Man Wai Bldg 702)

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 machine learning. Topics include Regression, Dimensionality Reduction, Optimization and Neural Networks, Classification, Clustering, Decision Tree Learning and Deep Learning. 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

• Mathematical Foundations for Data Analysis, Jeff M. Phillips, Springer 2021.
• Fundamentals of Data Analytics With a View to Machine Learning, Rudolf Mathar, Gholamreza Alirezaei, Emilio Balda, Arash Behboodi, Springer, 2020
• "Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Cambridge University Press.
• Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, The MIT Press, 2016.

References

• Richard Duda, Peter Hart and David Stock,Pattern Classification, Wiley-Interscience, 2nd Edition, 2015.
• Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014
• Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.
• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

Pre-class Notes

• linear approximation
• PDF
• Estimation
• Estimation_MLE
• Classfication
• Gradient Descent
• Gradient Descent
• Cross validation
• Bayes
• Bayes Regression
• k-means clustering
• SVM-read this (Nov 15, 2021)
• K-NN
• PCA
• Probability
• Mixtures of Gaussians
• Mixtures of Gaussians (Video)
• Introduction to Deep Learning (MIT)
• Machine learning and Data Mining (Lecture Notes)
• Machine Learning and Data Mining (Course Page)

Lecture Notes

• Notes1 (Basic)
• Notes2 (Standard)
• Notes3 (Advanced-only for reference)

Class Notes

• Introduction to course-Sept2, 2024
• Introduction to Data science
• Spectral Theorem
• SVD (MIT)
• K-means
• Topics in Matrix Theory(SVD)
• Cholesky decomposition
• Netflix Problem
• Probability Theory (Introduction)
• Optimization for Machine Learning (ENS)
• General EM algorithm
• SVM
• Machine Learning and Data Mining
• Notes on Linear Algebra (Jean Walrand)
• Linear Algebra
• More on Multivariate Gaussians (Stanford)
• The Rank-Nullity Theorem

Tutorial Notes

• Notes on linear algebra & Google Group
• Notes on SVD
• Notes on Optimization
• Notes on Taylor Theorem
• Notes on Probability
• Notes on Parameter Estimation
• Notes on Cross-validation
• Notes on PCA

Assignments

• Homework 1
• Homework 2
• Homework 3
• mini project 1
• mini project 2
• mini project 3
• mini project 4
• mini project 5

Quizzes and Exams

• Midterm Suggested Solution

Solutions

• Solutions 1
• Solutions 2
• Solutions 3
• mini project demo

Assessment Scheme

Tutorial attendance & good efforts		10%
Mid-Exam		12.5%
Homework/Project		27.5%
Final Exam		50%

Useful Links

• Fundamentals of Data Analytics With a View to Machine Learning
• Mathematical Foundations for Data Analysis
• Foundation of Data Science
• A Comprehensive Guide to Machine Learning
• PCA
• K-means
• K-Medoids
• Mixtures of Gaussian
• scikit-learn Machine Learning in Python
• Mixtures of Gaussian
• Hidden Markov Models
• Support Vector Machines(Andrew Ng)
• Machine Learning(Andrew Ng)
• Hidden Markov Models
• Neural Networks and Introduction to Deep Learning
• CNN-Li Feifei
• Deep Learning (Adrew Ng)
• LSTM
• Introduction to Machine Learning
• Lasso
• Machine Learning for OR & FE (Columbia University)
• CS229: Machine Learning (Stanford)
• Mathematics for Machine Learning
• Introduction to machine learning
• Introduction to Machine Learning

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 19, 2024 14:20:25