Optimization for Machine Learning

Optimization for Machine Learning by S.V.N. Vishwanathan - Machine Learning Summer School at Purdue, 2011. Machine learning poses data driven optimization problems. Computing the function value and gradients for these problems is challenging because they often involves thousands of variables and millions of training data points. This can often be cast as a convex optimization problem. Therefore, a lot of recent research has focused on designing specialized optimization algorithms for such problems. In this talk, I will present a high level overview of a few such algorithm that were recently developed. The talk will be broadly accessible and will have plenty of fun pictures and illustrations!

Lecture 1 - Introduction to Convexity
Lecture 2 - Introduction to Convexity, Support Vector Machine Training
Lecture 3 - Bundle Methods
Lecture 4 - Bundle Methods, Quasi-Newton Methods
Lecture 5 - Quasi-Newton Methods

Machine Learning Summer School at Purdue, 2011
A Machine Learning Approach for Complex Information Retrieval Applications
A Short Course on Reinforcement Learning
Classic and Modern Data Clustering
Divide and Recombine for the Analysis of Big Data
Graphical Models for the Internet
Introduction to Machine Learning
Large-Scale Machine Learning and Stochastic Algorithms
Machine Learning for a Rainy Day
Machine Learning for Discovery in Legal Cases
Machine Learning for Statistical Genetics
Mining Heterogeneous Information Networks
Modeling Complex Social Networks
Optimization for Machine Learning
Privacy Issues with Machine Learning: Fears, Facts, and Opportunities
Survey of Boosting from an Optimization Perspective
The MASH Project