Graduate Machine Learning Course at the University of Rhode Island
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
Convex Hull Construction
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
Artificial Neural Networks I
Artificial Neural Networks II
Artificial Neural Networks III
Decision Trees
Ensemble Methods
SVM Regression Part I
SVM Regression Part II
ANNs for Regression
Regression Trees
Deep Learning
Unsupervised Learning
Self-Organizing Maps