MATH3320  Foundation of Data Analytics  2019/20
Announcement
 Course Online [Download file]
 Midterm exam is scheduled on 12:30pm  2:00pm, 29 Oct in AB1G03. The exam covers all the materials taught in lectures (mainly lect 1 and lect 3:chp16)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: https://www.math.cuhk.edu.hk/course_builder/1920/math3320/z3411notes.pdf
 Please hand in the reports of the projects by sending emails to TAs before deadline.
General Information
Lecturer

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: M9:3010:15; T12:3014:15
 Tutorial: M8:309:15, AB1G03
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.
Textbooks
 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.:
References
 Shai ShalevShwartz and Shai BenDavid, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014
 Richard Duda, Peter Hart and David Stock,Pattern Classification, WileyInterscience, 2nd Edition, 2015.
 Tom Mitchell, Machine Learning, 1st Edition, McGrawHill, 1997
 Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. "Mathematics for Machine Learning." (2018).
Preclass Notes
 linear approximation
 Estimation
 Estimation_MLE
 Classfication
 Gradient Descent
 Gradient Descent
 Cross validation
 Bayes
 Bayes Regression
 kmeans clustering
 SVM
 KNN
Lecture Notes
Class Notes
 Notes on Linear Algebra (Jean Walrand)
 Linear Algebra
 Topics in Matrix Theory(SVD)
 More on Multivariate Gaussians (Stanford)
Tutorial Notes
Assignments
Solutions
Useful Links
 Introduction to Machine Learning
 Foundation of Data Science
 A Comprehensive Guide to Machine Learning
 Introduction to Monte Carlo
 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
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 25, 2019 09:59:17