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

• Public Data Sets: Machine Learning Repository, Datamob, Public data on google, Research pipeline (wiki: Data Sets), Statsci.org
• S4S, Wiki, Bengio's Lecture, Video Lecture: (Marcus Hutter)
• Department of ML @CMU
• Interpreting diagnostic tests (ROC curve)
• Fawcett: ROC101, Intro to ROC curve

Sample R codes and Freewares for learning

• Cateogrizing variables: BodyFat, data: bfpa.txt)
• Data Spliting (Training/Testing)
• Weka:  Website, Download
• C4.5 (Quilan): Building Classification Models: ID3 and C4.5, Tutorial, C4.5-ofai download

References

• Hastie, Tibshirani and Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd Edition. Springer-Verlag. (TextBook)