MATH3320  Foundation of Data Analytics  2022/23
Announcement
 There is no tutorial on Tuesday, September 6, 2022..
 Midterm: 13:0014:15 (75 minutes), Tuesday, October 25, 2022 (Cheng Yu Tung Building LT1B). The midterm covers notes 1 and chapter 1 of notes 2.
General Information
Lecturer

Prof. Zeng Tieyong
 Office: LSB225
 Tel: 39437966
 Email:
Teaching Assistant

Zeyu Li
 Office: LSB 222A
 Tel: 3943 3575
 Email:

Shen Mao
 Office: AB1 614
 Tel: 3943 4109
 Email:
Time and Venue
 Lecture: Mo 9:30AM  10:15AM (Mong Man Wai Eng Bldg 404); Tu 12:30PM  2:15PM (Cheng Yu Tung Building LT1B)
 Tutorial: Tu 11:30AM  12:15AM (Cheng Yu Tung Building LT1B)
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 mathematical foundations of probability, linear approximation and its polynomial and high dimensional extensions, 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
 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, WileyInterscience, 2nd Edition, 2015.
 Shai ShalevShwartz and Shai BenDavid, 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.
Preclass Notes
 linear approximation
 Estimation
 Estimation_MLE
 Classfication
 Gradient Descent
 Gradient Descent
 Cross validation
 Bayes
 Bayes Regression
 kmeans clustering
 SVMread this (Nov 15, 2021)
 KNN
 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)
 SVD (Sept. 20, 2022)
 kNN (Oct 17, 2022)
 Decision Tree (Oct 24, 2022)
 Multivariant Gaussian (Oct 31, 2022)
 PCA (Nov. 8, 2022)
 Introduction to Deep Learning (Nov. 14, 2022)
 SVM (in Chinese) Nov. 21, 2022
 Kmeans (Nov. 28, 2022)
Class Notes
 Notes on Linear Algebra (Jean Walrand)
 Linear Algebra
 Topics in Matrix Theory(SVD)Sept92021
 More on Multivariate Gaussians (Stanford)
 The RankNullity Theorem
 Spectral Theorem
 Cholesky decompositionSept82021
 SVD (MIT)Sept92021
 Probability Theory (Introduction)
 Optimization for Machine Learning (ENS)
 General EM algorithm
 SVM
 Machine Learning and Data Mining
Tutorial Notes
 Notes on Linear Algebra
 Notes on SVD
 Notes on Taylor Theorem for High Dimension
 Notes on Optimization and Probability
 Notes on Machine Learning
 Notes on Multivariate Gaussian
 Notes on PCA
 Notes on Backpropagation
 Notes on Linear Classification
Assignments
Quizzes and Exams
Solutions
 Solutions 1
 Solutions 2
 Solutions 3
 Solutions 4
 Solutions 5
 Solutions 6
 Solutions 7
 Solutions 8
 Solutions 9
Assessment Scheme
Tutorial attendance & good efforts  10%  
MidExam  12.5%  
Project  12.5%  
Final Exam  65%  
Backup Plan: In case facetoface teaching and assessment is not possible due to the pandemic, the assessment will be changed to: Tutorial and homework 30%; Midterm 35% ; Project 35%  % 
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
 Kmeans
 KMedoids
 Mixtures of Gaussian
 scikitlearn 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
 CNNLi 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 28, 2022 10:29:55