High Dimensional Data Analysis

Henry Horng-Shing Lu (盧鴻興)

Institute of Statistics, National Chiao Tung University, Fall, 2013

TEL: 03-5731870 EMAIL: hslu@stat.nctu.edu.tw

　Goals :

This course will introduce state-of-art techniques of high dimensional data analysis. Systematical introduction will be provided from classical to modern methods. Methodology explanations will be given with computation codes in Matlab, R or the other platforms. 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. 　

• Introduction
• Supervised learning
• Unsupervised learning
• Dimension reduction
• Network analysis

• T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning (2009). 2nd ed.
  Springer-Verlag.
• E. Alpaydin. Introduction to Machine Learning (2010). 2nd ed. MIT Press.
  
• T. Cai and X. Shen (editors). High-Dimensional Data Analysis (2010) World Scientific.
  
• P. Bühlmann and S. van de Geer. Statistics for high-dimensional data: methods, theory and
  applications (2011). Springer.
• S.-Y. Huang, K.-Y. Lee and H. H.-S. Lu. Lecture Notes on Statistical and Machine Learning
  (draft version).

• Homework: 50%
• Term Project: 50%

• 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
• Weka: Free Data Mining Software in Java
• Virtual library of statistics
• R: a free software environment for statistical computing and graphics
• Data in UCI Machine Learning Repository