Topics in Machine Learning

CSci 8980 Presentation Schedule

Date	#	Topic	Presenter
Tu 01/17/06		Course Overview	Arindam Banerjee
Th 01/19/06		Online Algorithms in Machine Learning Avrim Blum Game Theory, On-line Prediction and Boosting Yoav Freund and Robert Schapire	Arindam Banerjee Slides Notes
Tu 01/24/06	1.	The Weighted Majority Algorithm Nick Littlestone and Manfred Warmuth	Amrudin Agovic Slides
Th 01/26/06	2.	A Decision-Theoretic Generalization of Online Learning and an Application to Boosting Yoav Freund and Robert Schapire	Tim Miller Slides Additional Slides used from Rob Schapire's class
Tu 01/31/06	3.	Exponentiated Gradient versus Gradient Descent for Linear Predictors Jyrki Kivinen and Manfred Warmuth	Maitreyi Nanjanath Slides
Th 02/02/06	4.	Online Portfolio Selection using Multiplicative Updates David Helmbold, Robert Schapire, Yoram Singer and Manfred Warmuth	Ryan McCabe Slides
Tu 02/07/06	5.	Regret in Online Decision Problems Dean Foster and Rakesh Vohra	Thomas Whipple Slides
Th 02/09/06		Online Learning: Wrap up Boosting: Interpretations, Extensions, Theory	Arindam Banerjee Slides
Tu 02/14/06		Convex Analysis and Optimization I Project Proposals due	Arindam Banerjee Slides (both lectures)
Th 02/16/06		Convex Analysis and Optimization II	Arindam Banerjee
Tu 02/21/06	6.	Relative Loss Bounds for Multidimensional Regression Problems Jyrki Kivinen and Manfred Warmuth	Arindam Banerjee Slides
Th 02/23/06	7.	Logistic Regression, AdaBoost and Bregman distances Michael Collins, Robert Schapire and Yoram Singer	Arindam Banerjee Slides
Tu 02/28/06	8.	Clustering with Bregman Divergences Arindam Banerjee, Srujana Merugu, Inderjit Dhillon and Joydeep Ghosh	Rohit Gupta Slides
Th 03/02/06		Large Margin and Kernel Methods (Basics)	Arindam Banerjee Slides (both lectures)
Tu 03/07/06		Large Margin Methods (Theory)	Arindam Banerjee Notes
Th 03/09/06	9.	An Introduction to Kernel-Based Learning Algorithms Klaus-Robert Muller, Sebastian Mika, Gunnar Ratsch, Koji Tsuda, Bernhard Scholkopf	Joanna Giforos Slides
Tu 03/14/06		Spring Break
Th 03/16/06		Spring Break
Tu 03/21/06	10.	Boosting as a Regularized Path to a Maximum Margin Classifier Saharon Rosset, Ji Zhu and Trevor Hastie	Charles Olson Slides
Th 03/23/06	11.	Large Margin Classification using the Perceptron Algorithm Yoav Freund and Robert Schapire Mid-Sem Project Progress Reports Due	Amit Bose Slides
Tu 03/28/06	12.	Hidden Markov Support Vector Machines Yasemin Altun, Ioannis Tsochantaridis and Thomas Hofmann	Varun Chandola Slides
Th 03/30/06	13.	Large Margin Methods for Structured Classification: Exponentiated Gradient Algorithms and PAC-Bayesian Generalization Error Bounds Peter Bartlett, Michael Collins, Ben Taskar, and David McAllester NIPS version of the paper (has plots)	Yu Jin Slides
Tu 04/04/06		Inference/Decoding on Graphs (Based on the Sum-product and GDL papers)	Arindam Banerjee Slides (from IMA talk by Frank Kschischang)
Th 04/06/06	14.	Calibrated Learning and Correlated Equilibrium Dean Foster and Rakesh Vohra	Jason Sorensen Slides
Tu 04/11/06	15.	A Simple Adaptive Procedure Leading to Correlated Equilibrium Sergiu Hart and Andreu Mas-Colell	Arindam Banerjee Slides
Th 04/13/06	16.	Correlated Equilibria in Graphical Games Sham Kakade, Michael Kearns, John Langford and Luis Ortiz	Stephen Damer Slides
Tu 04/18/06	17.	Game Theory, Maximum Entropy, Minimum Discrepancy and Robust Bayesian Decision Theory Peter Grunwald and Philip Dawid	Arindam Banerjee Slides
Th 04/20/06	18.	Query Incentive Networks Jon Kleinberg and Prabhakar Raghavan	Nishith Pathak Slides
Tu 04/25/06		Project Presentations	Rudy & Steve Amit & Maitreyi
Th 04/27/06		Project Presentations	Yu & Nishith Ryan & Tim Jason Tom
Tu 05/02/06		Project Presentations	Varun & Rohit Joanna Charlie
Th 05/04/06		Wrap-up and Discussions	Arindam Banerjee
Tu 05/09/06		Final Project Reports Due