CS6464: Concepts In Statistical Learning Theory  
January  May, 2018 
Textbooks 
V. N. Vapnik; Statistical Learning Theory. Wiley, 1998. 
T. Hastie, R.Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference and Prediction", Springer Series in Statistics, 2009. 
Kevin R Murphy, "Machine Learning  A Probabilistic Perspective", The MIT Press, 2012. 

References 
Michael J. Kearns and Umesh Vazirani; An Introduction to Computational Learning Theory; The MIT Press, 1994. 
Journal of the Royal Statistical Society: Series B (Statistical Methodology). 
Foundations and Trends in Machine Learning; Now Publishers Inc. 
Journal of Machine Learning Research; JMLR, Inc. and Microtome Publishing (United States). 
Bishop, Christopher M. "Pattern recognition and machine learning", Springer, 2006. 
Conference Proceedings of ICML, NIPS. 
R.O. Duda, P.E. Hart and D.G. Stork "Pattern Classification (2nd ed.)", John Wiley & Sons, Inc., 2003. 

Basic Statistics and Theorems  
Least Square Regression, BiasVariance Tradeoff  
Clustering Techniques  
LAR and LASSO  
L_1 regularization techniques  
Sparse Coding  
SVM  
Software Assignment 1
Problem Statement :
Data for Question 1 :
Q1_data_01.Rda
Q1_data_02.Rda
Software Assignment 2
Problem Statement :
Allotment of learning techniques :
Click here to go the Data Repository page
Software Assignment 3
Problem Statement :
Dataset :
Assignment3Data
Marks Distribution
Tutorial Dates
Logistic Details
Important Dates  
Extra Classes: 
February 17,2018 (10:30am12:30pm) & April 8,2018 (10:30am12:30pm) 
Mid Semester Exam: 
March 21,2018 (3:304:30pm) 
End Semester Exam: 
May 11,2018 (9:00am12:00pm) 
Software Assignment 1: 
February 15,2018 
Software Assignment 2: 
March 15,2018 
Software Assignment 3: 
April 27,2018 
Self Study Topics