CSC 581 - Lecture Notes

Introduction
Statistical Computing with R
Computational Aspects of Knowledge Discovery
Elements of Linear Algebra
Decision Surfaces and Functions
Simple Learning
Perceptron Learning
Duality
Maximum Margin Classifiers
Quadratic Programming
Lagrangian Optimization
Dual Maximum Margin Optimization
Linear SVMs
Non-linear SVMs
Kernel Functions
Soft-Margin SVMs
Model Evaluation, Part I
Model Evaluation, Part II
Model Evaluation, Part III
Implementation Part I
Implementation Part II
Elements of Statistical Learning Theory
Multi-Class SVM's
SVM Regression Part I
SVM Regression Part II
Convex Hull Construction
Artificial Neural Networks I
Artificial Neural Networks II
Decision Trees
Alternative Formulations of SVMs