Machine Learning Lunch

	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

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

We would like to thank our sponsors:

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Fall 2007 ML talk organizing committee

• Vitor Carvalho
• Steve Hanneke
• Andreas Krause
• Jure Leskovec
• Sajid Siddiqi

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

• Machine Learning Seminar Series
• Intelligence Seminar Series
• Statistics Seminars
• Bioinformatics
• School of Computer Science (SCS) Seminars
• Robotics Institute (RI)Seminars
• Center for the Neural Basis of Cognition (CNBC) Seminars
• VASC Seminar Series
• Language Technologies Institute (LTI) Sponsored Seminars
• Human-Computer Interaction Institute (HCII) Seminar Series

Conferences

Lists of conferences

David Aha's ML/CBR Conference Announcements
ÖFAI Machine Learning Conference List
Neural Network, Vision, And Speech Conferences
IEEE Conference Database

ICML-2003

International Conference on Machine Learning
conference will be August 21 - August 24, 2003 in Washington, DC
abstract submission deadline is February 10, 2003
paper submission deadline is February 14, 2003

KDD-2003

International Conference on Knowledge Discovery and Data Mining
conference will be August 24-27, 2003 in Washington, DC
abstract submission deadline is Feb. 21, 2003
paper submission deadline is Feb. 28, 2003

COLT 2003

Conference on Computational Learning Theory
conference will be August 24-27, 2003 in Washington, DC
submissions are due March 23, 2003

NIPS 2003

Neural Information Processing Systems
tutorials will be December 8, 2003
main conference will be December 9-11, 2003
workshops will be December 12-13, 2003
paper submission deadline is June 6, 2003

AAAI 2002

National Conference on Artificial Intelligence
conference will be July 28 - August 1, 2002 in Edmonton, Alberta, Canada
electronic submission of abstracts deadline is January 22, 2002
paper submission deadline is January 23, 2002

IJCAI-01

International Joint Conference on Artificial Intelligence
conference will be August 5 - August 10, 2001 in Seattle, Washington
electronic title page submission deadline for the 1999 conference was January 12, 1999
paper submission deadline for the 1999 conference was January 14, 1999

UAI-2002

Conference on Uncertainty in Artificial Intelligence
conference will be August 1 - 4, 2002 at the University of Alberta, Edmonton, Canada
abstract submission deadline is March 18, 2002
paper submission deadline is March 25, 2002

ILP 2002

International Conference on Inductive Logic Programming
conference will be July 9-11, 2002 in Sydney, Australia
paper submission deadline is February 15, 2002

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   (Hierarchical topic index by Vivisimo.)
• 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 Michigan State
• Other RL repositories: U Mass | 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

• CMU Robot Learning Lab
• CMU Coral Research Group
• Workshop papers: ML94 Robot Learning

Related Sites

• Cambridge Control Engineering
• On-line CS Techreports