Statistical Machine Learning: Theory and Applications
Fall 2010
Course Info
Syllabus
Navigator: C.
Andy Tsao.
Office: SE A411. Campus Tel: 3520
Lectures: Tue 1310-1600 @ SE
A208
Office Hours: Mon 1210-1300, Tue 1110-1200, 1610-1700; Thr 1510-1600 @ 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)
- Final
- Date: Jan 11 (Tue) 1310-1500 @ SE-A208.
- Material:
Chapter 1-4, Chapter 9-10 and class materials such as Boosting and SVM.
Basic knowledge regarding R implementation for linear classification
methods and SVM (e1071) and AdaBoost (Ada).
- You are allowed to bring an A4 cheatsheet, calculator and
translator with you.
- The final project team report is dued on Jan 18 (Mail in by 2359 and confirm my reception).
Class"Notes: Week 1-2, Week 3, Week 4-5, Boosting (consistency, AHCBoost), Finale
(Boosting, SVM and Project Instructions)
Assignments
Data Sets and Supplemental
Sample R codes and Freewares for learning
References
Last
modified: 110106