MATH3320 - Foundation of Data Analytics - 2018/19

Announcement

• Course Online [Download file]
• Midterm exam is scheduled on 12:30pm - 2:15pm, 23 Oct in AB1-G03. The exam covers all the materials taught in lectures and tutorials.
• The deadline of the project report is 19th Nov.

---

General Information

Lecturer

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

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.

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 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

Pre-class Notes

• linear approximation
• PDF
• Estimation
• Estimation_MLE
• Classfication
• Gradient Descent
• Gradient Descent
• Cross validation
• Bayes
• Bayes Regression
• k-means clustering
• SVM

Lecture Notes

• Notes1
• Notes2
• Notes3

Class Notes

• Notes on Linear Algebra (Jean Walrand)

Tutorial Notes

• Tutorial 1
• Tutorial 2
• Tutorial 3
• Tutorial 4
• Tutorial 5
• Tutorial 6
• Tutorial 7

Assignments

• Exercise 1
• Project

Solutions

• Midterm Solution

Useful Links

• Introduction to Machine Learning
• Foundation of Data Science
• A Comprehensive Guide to Machine Learning
• Introduction to Monte Carlo
• PCA
• K-means
• K-Medoids
• Mixtures of Gaussian
• http://scikit-learn.org/stable/index.html
• 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

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: January 14, 2019 16:42:54