**Statistical Learning **

Institute of

Tel: 03-5731870 Email: hslu@stat.nctu.edu.tw

__Goals:__

This course will introduce state-of-art techniques of statistical learning with
kernel methods. Systematical introduction will be provided from classical to
modern methods. Methodology explanations will be given with computation codes
in Matlab, R, Weka and other applications packages. Hands-on experience is emphasized using
illustrations and reproducible examples. Undergraduate knowledge of probability
and statistics will be helpful for the understanding of this course. Related
materials will be post at the course web page in http://www.stat.nctu.edu.tw/people/bio.php?PID=16.

__Course
Outlines:__

- Introduction
- Supervised learning
- Unsupervised learning
- Dimension reduction
- Kernel methods

__References:__

- G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning: with Applications in R (2013). Springer-Verlag.
- T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning (2009). Second Edition, Springer-Verlag.
- E. Alpaydin. Introduction to Machine Learning (2004). MIT Press.
- S.-Y. Huang, K.-Y. Lee and H. H.-S. Lu. Lecture Notes on Statistical and Machine Learning (draft version).

__Evaluation:__

- Homework: 70%
- Term Project: 30%

__Links:__

- In-depth introduction to machine learning in 15 hours of expert videos
- Data in UCI Machine Learning Repository
- Kaggle Datasets
- The Human Face of Big Data
- Local links of statistical computing and statistics
- Kernel Statistics Toolbox
- Kernel Sliced Inverse Regression Package
- Wiki: machine learning, statistical learning, data mining ¡K
- The
Data Mine
- Data
Mining Software Comparison
- Weka: Free Data Mining
Software in Java