Here are notes and links for the extra topics we get to cover since we got through the material rapidly.
Support Vector Machines
General slides on SVM: chap5_alternative_classification.ppt
One-class SVM: One-ClassSVM10.1.1.39.912.pdf
Regression SVM: SVMRegressionTutorial10.1.1.4.7700.pdf
Methods for rapid solution of SVM: stochasticSubGradient-shalev-shwartz.pdf
Notes on Mercer Kernels: MercerKernelslecture18.pdf
Path Algo - Hastie:
Time Series
Some slides that will guide us through the material: Time Series.pdf
Here are links to R-code and data sets that we'll use.
SVM examples: svm1.R , oneClassSVM.R
Time series examples: general.R , linMod.R , unitRoot.R , garch.R , VIX.csv
Recorded Lecture
https://datamining.webex.com/datamining/ldr.php?AT=pb&SP=MC&rID=108067727&rKey=1031e3ec43de59fa