Statistical Learning (Autumn 2017)

Lecturer: Dong Liu
Teaching Assistants: Xusong Chen (, Huiqun Li (, Shaobo Min (, Feiyu Qin (
Mailbox for project reports:


Lectures will be given at 14:00 PM~16:40 PM each Thursday in classroom 3C201.


Chapter 1. Introduction
Chapter 2. Data Analysis
Chapter 3. Supervised Learning Theory
Chapter 4. Linear Regression
Chapter 5. Linear Classification
Chapter 6. Generative Bayesian Approaches
Chapter 7. Combining Models
Chapter 8. Artificial Neural Network
Chapter 9. Support Vector Machine
Chapter 10. Sparse Models
Chapter 11. Beyond Supervised Learning


All exercises: download
Iris flower data set: download


1. k-NN, dataset
2. Binary Classification, dataset
3. User and Movie, dataset
4. Image Retrieval, dataset


Pattern Recognition and Machine Learning (PRML) by C. M. Bishop. Book homepage
The Elements of Statistical Learning (ESL) by T. Hastie, R. Tibshirani, J. Friedman. Book homepage
机器学习, 周志华著. Book homepage


Online course (Machine Learning by Andrew Ng): coursera link, netease link
UCI Machine Learning Repository
scikit-learn, Machine Learning in Python
R: The R Project for Statistical Computing
MLC++, a library of C++ classes for supervised machine learning
Weka, Data Mining Software in Java