Applied Multivariate Statistical Analysis                                  

Course Info

• Syllabus     Navigator: C. Andy Tsao.   Office: SE A411.    Campus Tel: 3520
• Lectures: Mon 1310-1400, Thr 1310-1500@ SE A301
  
• Office Hours: Mon 1410-1500, Tue 1210-1300 @ SE A411 or by appointment
• Prerequisites: Statistics, Linear Algebra. Knowledge about regression or General/Generalized Linear Models will be helpful. 
• "Official" computing software: R (original, mirrors @ NTU, PU, Packages), Rstudio
  
• References: 
• Hardle and Simar (2003). Applied Multivariate Statistical Analysis, Springer. (@Sprigner, ExtraMaterial R codes, etc, Quantlet, Data Sets) (Textbook)
  
• Johnson and Wichern (2007). Applied Multivariate Statistical Analysis, 6th Edition. Pearson Education International.
• Hastie, Tibshirani and Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd Edition. (aka. ESLII)  Springer-Verlag.

Class slides, Homework, Data, Themes

• Statistical Machine Learning: Ensemble Learning (New)
• Project:  Project Guideline
• SVM, Kaggle, etc: Mo! Kno!, CoLearning (earlier O? Kno // will expire soon)

Sample R codes and Freewares

• R bloggers