MATH3320 - Foundation of Data Analytics - 2019/20
Announcement
- Course Online [Download file]
- Midterm exam is scheduled on 12:30pm - 2:00pm, 29 Oct in AB1-G03. The exam covers all the materials taught in lectures (mainly lect 1 and lect 3:chp1-6)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: M9:30-10:15; 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
- Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. "Mathematics for Machine Learning." (2018).
Pre-class Notes
- linear approximation
- Estimation
- Estimation_MLE
- Classfication
- Gradient Descent
- Gradient Descent
- Cross validation
- Bayes
- Bayes Regression
- k-means clustering
- SVM
- K-NN
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
- 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
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