Statistical Learning Theory

Mathematical Background

• MA 575: Linear Models taught at Boston University
• MA 751: Taught at Boston University by M. Kon, Math and Statistics Department Email: mkon@bu.edu

Reference Courses

• CS 4780: Machine Learning for Intelligent Systems by Cornell University
• Statistics 203- Stanford University's taugh courseware on Introduction to Regression Models and Analysis of Variance, covering topics Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Model Selection for Mupltiple Linear Models, Multiple Linear Regression -- Diagnostics, Analysis of Variance: Fixed Effects, Experimental Design, Penalized Regression, Robust Regression, Nonlinear Regression, Generalized Linear Models, Mixed Effects Models, Time Series Regression: Correlated Errors, Functional Linear Models, Additive Models
• Uncertainty, Design, and Optimization- Duke University taught curriculum.

Reference Textbooks:

• An Introduction to Statistical Learning- Authored by Gareth James, Daniela Witten ,Trevor Hastie and Robert Tibshirani
• The Elements of Statistical Leraning- Authored by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• Machine Learning: A Probabilistic Perspective- Authored by Kevin Murphy
• Understanding Machine Learning: From Theory to Algorithms- Authored by Shai Shalev-Shwartz and Shai Ben-David from The Hebrew University, Jerusalem and University of Waterloom Canada respectively

Advanced Textual References:

• Statistical Learning with Sparsity

References for Practical Sessions:

• Stanford Statistical Learning Software: This is a collection of R packages written by current and former members of the labs of Trevor Hastie, Jon Taylor and Rob Tibshirani. All of these packages are actively supported by their authors.

• J P Vert's Courses- This repository was handy when we were implementing the K-nearest neighbours for regression.

• Machine Learning algorithm implementation from scratch

Other Sources:

• Scatterplot Smooting

Read from UC Berkley corse repo