Machine Learning Lunch seminar Carnegie Mellon

Machine Learning Lunch

All talks will be held on Mondays at Noon in Newell Simon Hall 1507, unless otherwise noted.

Lunch and drinks provided!

Volunteer to give a talk today!

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Videos of newer talks will be hosted on VideoLecture.Net. Check out their videos of lectures, tutorials, summer schools, etc.

	Oct 6, 2008 Wean 4623KDD 2008 Conference Review Session Weighted Graphs and Disconnected Components: Patterns and a Generator Mary McGlohon
	Oct 6, 2008 Wean 4623KDD 2008 Conference Review Session Efficient Parallel Learning of Linear Dynamical Systems on SMPs Lei Li
	Sept 29, 2008 UAI/ACL 2008 Conference Review Session Feature Selection via Block-Regularized Regression Seyoung Kim
	Sept 29, 2008 UAI/ACL 2008 Conference Review Session Exploiting document structure and feature hierarchy for semi-supervised domain adaptation Andrew Arnold
	Sept 22, 2008 Kernelized Sorting Le Song

	May 5, 2008 Learning Patterns of the Brain: Machine Learning Challenges of fMRI Analysis Mark Palatucci
	Apr 28, 2008 mStruct: Inference of population structure in light of both genetic admixing and allele mutations Suyash Shringarpure
	Apr 28, 2008 Query-Specific Learning for Graphical Models Anton Chechetka
	Apr 14, 2008 Learning Driving Route Preferences Brian Ziebart
	Apr 7, 2008 Learning Stable Linear Dynamical Systems Sajid M. Siddiqi
	Mar 3, 2008 Probability Distributions on Permutations Jonathan Huang
	Feb 25, 2008 High Dimensional Sparse Regression and Structure Estimation Shuheng Zhou
	Feb 18, 2008 Discovering Cyclic Causal Models by Independent Components Analysis Gustavo Lacerda
	Feb 11, 2008 Overview of New Developments in Boosting Joseph Bradley
	Feb 4, 2008 Relational Learning as Collective Matrix Factorization Ajit Singh
	Jan 21, 2008 Structured Prediction: Maximum Margin Techniques Nathan Ratliff
	Dec 10, 2007 Maximum Likelihood Estimation in Latent Class Models for Contingency Table Data Yi Zhou
	Dec 3, 2007 Causal discovery based on non-gaussianity Patrick Hoyer
	Nov 26, 2007 Statistical Parsing Triptych: Jeopardy, Morphosyntax, and M-Estimation Noah Smith
	Nov 19, 2007 The Maximum Entropy Principle Miroslav Dudik
	Nov 12, 2007 Spatiotemporal Stochastic Processes and Their Prediction Cosma Shalizi
	Nov 05, 2007 Stochastic Processes and their Prediction Cosma Shalizi
	Oct 29, 2007 Proximity on Graphs: Definitions, Fast Solutions and Applications Hanghang Tong
	Oct 22, 2007 Machine Learning in in vivo CNS Drug Discovery Jeff Schneider
	Oct 16, 2007 (Tue) Visualizing Social Media: Principles and Techniques Matthew Hurst
	Oct 1, 2007 Random Walks on Graphs: A General Overview Purnamrita Sarkar
	Sept 24, 2007 Some Topics in Spam Filtering D. Sculley
	May 7, 2007 Probabilistic Inference in Distributed Systems Stanislav Funiak
	Apr 23, 2007 Learning without the loss function John Langford
	Apr 16, 2007 Sparsity recovery and structure learning Pradeep Ravikumar
	April 2, 2007 A unifying view of component analysis (from a computer vision perspective) Fernando De la Torre
	Mar 19, 2007 Active Learning of Binary Classifiers Nina Balcan
	Mar 5, 2007 Features, kernels, and similarity functions Avrim Blum
	Feb 26, 2007 Models of real-world networks (Part II) Jure Leskovec
	Feb 19, 2007 The structure and function of real-world graphs and networks (Part I) Jure Leskovec
	Feb 12, 2007 Discrete Markov Random Fields -- the Inference story Pradeep Ravikumar
	Jan 22, 2007 NIPS 2006 Conference Review Session.
	Jan 22, 2007 Greedy Layer-Wise Training of Deep Networks. Nathan Ratliff
	Jan 22, 2007 Approximate inference using planar graph decomposition. Pradeep Ravikumar

Thanks to VideoLecture.Net, the video recordings of our newer talks will be hosted on their website.

We would like to thank our sponsors:

Fall 2007 ML talk organizing committee

Past talks

The 2006 ML talks.
The 2005 ML talks.
The 2004 ML talks.
The 2003 ML talks.
The 2002 ML talks.
The 2001 ML talks.
The 2000 ML talks.
The 1999 ML talks.
Older talks (1992-93, 1993-94, 1994-95, 1995-96, 1996-97, 1997-98)

Related Seminars

Conferences

Lists of conferences: David Aha's ML/CBR Conference Announcements
Neural Network, Vision, And Speech Conferences
IEEE Conference Database
ICML -- International Conference on Machine Learning
KDD -- International Conference on Knowledge Discovery and Data Mining
COLT -- Conference on Computational Learning Theory
NIPS -- Neural Information Processing Systems
AAAI -- National Conference on Artificial Intelligence
IJCAI -- International Joint Conference on Artificial Intelligence
UAI -- Conference on Uncertainty in Artificial Intelligence
ILP -- International Conference on Inductive Logic Programming

Resources

General Machine Learning Resources

David Aha's List of Machine Learning Resources
ÖFAI Machine Learning Resources
The UCI Repository
Machine Learning Department, CMU
Machine Learning Journal
SGI's MLC++ (a library of machine learning code)
CMU Learning Lab
Home pages for people in Machine Learning
CMU StatLib
MachineLearning.com
LearningTheory.org
Tom Minka: Statistical Learning / Pattern Recognition Glossary and Papers and Tutorial Notes
Sam Roweis: Tutorial Notes

Reinforcement Learning Resources

RL papers by CMU people (including a handy survey of RL)
The excellent Reinforcement Learning Repository at University of Massachusetts, Amherst
Other RL repositories: Rich Sutton | Neuroprose
Workshop info: Spring 1996 NSF Workshop on Reinforcement Learning

Support Vector Machine Resources

Kernel-Machines.org with pointers to Tutorials
Support Vector Machines the book
Learning with Kernels the book - support vector machines, regularization, optimization and beyond

Robot Learning Resources

Related Sites