PURDUE Machine Learning Summer School 2011
The coverage of the summer school is very impressive. The lecture titles and presenters were:
- Machine Learning for Statistical Genetics by Karsten Borgwardt
- Large-scale Machine Learning and Stochastic Algorithms by Leon Bottou
- Divide and Recombine (D&R) for the Analysis of Big Data by William S. Cleveland
- Privacy Issues with Machine Learning: Fears, Facts, and Opportunities by Chris Clifton
- The MASH project. An open platform for the collaborative development of feature extractors by Francois Fleuret
- Techniques for Massive-Data Machine Learning, with Application to Astronomy by Alex Gray
- Mining Heterogeneous Information Networks by Jiawei Han
- Machine Learning for a Rainy Day by Sergey Kirshner
- Machine Learning for Discovery in Legal Cases by David D. Lewis
- Classic and Modern Data Clustering by Marina Meilă
- Modeling Complex Social Networks: Challenges and Opportunities for Statistical Learning and Inference by Jennifer Neville
- Using Heat for Shape Understanding and Retrieval by Karthik Ramani
- Learning Rhythm from Live Music by Christopher Raphael
- Introduction to supervised, unsupervised and partially-supervised training algorithms by Dale Schuurmans
- A Machine Learning Approach for Complex Information Retrieval Applications by Luo Si
- A Short Course on Reinforcement Learning by Satinder Singh Baveja
- Graphical Models for the Internet by Alexander Smola
- Optimization for Machine Learning by S V N Vishwanathan
- Survey of Boosting from an Optimization Perspective by Manfred K. Warmuth
Now that would be a summer school to remember!