**Statistical Learning **

Institute of

Tel: 03-5731870 Email: henryhslu@nycu.edu.tw

__Goals:__

This course will introduce state-of-art techniques of statistical learning with kernel methods. Systematic introduction will be provided from classical to modern methods. Methodology explanations will be given with computation codes in R 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 posted at the course web page.

__Course
Outlines:__

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

__Hybrid course:__

- Meeting classroom: A427, Thursday, 1:20-4:20pm, Spring, 2023
- Online Google Meet link: https://meet.google.com/cia-mdwr-oby

__References:__

- G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning: with Applications in R, 2nd edition (2021). Springer-Verlag.
- T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning (2009). Second Edition, Springer-Verlag.
- K.-T. Tsai, Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction(2021). Chapman and Hall/CRC.
- E. Alpaydin. Introduction to Machine Learning (2004). MIT Press.
- Data Science (數據科學)
- Machine Learning (機器學習)
- Artificial Intelligence (人工智慧)
- Biomedical Applications (生醫運用)

__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 …
- The
Data Mine
- Data
Mining Software Comparison
- Weka: Free Data Mining
Software in Java