MLSS '05 - Chicago

Machine Learning Summer School 2005 - Chicago

Machine learning is a field focused on making machines learn to make predictions from examples. It combines elements of mathematics, computer science, and statistics with applications in biology, physics, engineering and any other area where automated prediction is necessary. This short summer school is an intense introduction to the basics of machine learning and learning theory with various additional advanced topics covered. It is appropriate for anyone interested in learning this material.

Categories

Top » Computer Science » Machine Learning


Introduction
[syn] 125 views, 35:49 WelcomeWelcome David McAllester, John Langford David McAllester, John Langford	225 views, 12:40 Welcome to Chicago, and a (brief!) introduction to machine learningWelcome to Chicago, and a (brief!) introduction to machine learning John Langford John Langford
Lectures
[syn] 4440 views, 1:37:46 Online Learning and Game TheoryOnline Learning and Game Theory Adam Kalai Adam Kalai 3 comments	[syn] 331 views, 1:42:36 Generalization boundsGeneralization bounds John Langford John Langford	[syn] 1410 views, 1:00:56 Introduction to Kernel MethodsIntroduction to Kernel Methods Mikhail Belkin Mikhail Belkin	1234 views, 47:32 Semi-supervised Learning, Manifold MethodsSemi-supervised Learning, Manifold Methods Mikhail Belkin Mikhail Belkin	2005 views, 1:21:57 Introduction to Kernel MethodsIntroduction to Kernel Methods Partha Niyogi Partha Niyogi 5 comments
780 views, 54:52 Semi-supervised Learning, Manifold MethodsSemi-supervised Learning, Manifold Methods Partha Niyogi Partha Niyogi 1 comment	[syn] 152 views, 1:04:59 Evidence Integration in BioinformaticsEvidence Integration in Bioinformatics Phil Long Phil Long	199 views, 2:01:19 Empirical Comparisons of Learning Methods & Case StudiesEmpirical Comparisons of Learning Methods & Case Studies Rich Caruana Rich Caruana	[syn] 8622 views, 2:07:12 BoostingBoosting Robert Schapire Robert Schapire 9 comments	[syn] 863 views, 1:50:23 Trees for Regression and ClassificationTrees for Regression and Classification Robert Nowak Robert Nowak
112 views, 49:49 Algorithms for Learning and their EstimatesAlgorithms for Learning and their Estimates Steve Smale Steve Smale	597 views, 3:38:44 Energy-based models & Learning for Invariant Image RecognitionEnergy-based models & Learning for Invariant Image Recognition Yann LeCun Yann LeCun	396 views, 1:23:27 Online Learning with KernelsOnline Learning with Kernels Yoram Singer Yoram Singer 1 comment	116 views, 53:58 Fingerprints of Rhthm in Natural LanguageFingerprints of Rhthm in Natural Language Antonio Galves Antonio Galves	1006 views, 55:35 Diffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical SystemsDiffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical Systems Boaz Nadler Boaz Nadler 4 comments
256 views, 50:51 Learning to SignalLearning to Signal Brian Skyrms Brian Skyrms	[syn] 4596 views, 32:36 The Dynamics of AdaBoostThe Dynamics of AdaBoost Cynthia Rudin Cynthia Rudin 3 comments	80 views, 53:01 Learning variable covariances via gradientsLearning variable covariances via gradients Ding-Xuan Zhou Ding-Xuan Zhou	90 views, 1:05:34 Feasible Language LearningFeasible Language Learning Ed Stabler Ed Stabler	126 views, 1:10:31 On the evolution of languagesOn the evolution of languages Felipe Cucker Felipe Cucker
[syn] 139 views, 55:17 Game Dynamics with Learning and Evolution of Universal GrammarGame Dynamics with Learning and Evolution of Universal Grammar Garrett Mitchener Garrett Mitchener	[syn] 155 views, 56:08 Some Aspects of Learning Rates for SVMsSome Aspects of Learning Rates for SVMs Ingo Steinwart Ingo Steinwart	197 views, 51:40 Categorical Perception + Linear Learning = Shared CultureCategorical Perception + Linear Learning = Shared Culture Mark Liberman Mark Liberman	181 views, 58:48 Multiscale analysis on graphsMultiscale analysis on graphs Mauro Maggioni Mauro Maggioni	[syn] 71 views, 53:48 Adventures with CamilleAdventures with Camille Peter Culicover Peter Culicover
116 views, 51:50 On Optimal Estimators in Learning TheoryOn Optimal Estimators in Learning Theory Vladimir Temlyakov Vladimir Temlyakov	[syn] 1590 views, 2:38:48 Tutorial on Machine Learning ReductionsTutorial on Machine Learning Reductions John Langford John Langford 3 comments	[syn] 4185 views, 1:35:21 Information GeometryInformation Geometry Sanjoy Dasgupta Sanjoy Dasgupta 3 comments	[syn] 2256 views, 59:35 On the Borders of Statistics and Computer ScienceOn the Borders of Statistics and Computer Science Peter J. Bickel Peter J. Bickel 1 comment	6049 views, 1:24:46 Bayesian LearningBayesian Learning Zoubin Ghahramani Zoubin Ghahramani 9 comments
108 views, 1:44:36 Learning on Structured DataLearning on Structured Data David McAllester David McAllester 1 comment	1141 views, 1:16:33 Learning on Structured DataLearning on Structured Data Yasemin Altun Yasemin Altun 1 comment	587 views, 1:24:18 An introduction to grammars and parsingAn introduction to grammars and parsing Mark Johnson Mark Johnson 3 comments	71 views, 41:36 Learning patterns in omic data: applications of learning theoryLearning patterns in omic data: applications of learning theory Sayan Mukherjee Sayan Mukherjee
Interviews with students
614 views, 04:16 Short interviews MLSS05 Chicago by John LangfordShort interviews MLSS05 Chicago by John Langford John Langford John Langford
Debates
345 views, 1:23:45 Lunch debate 23.5.2005Lunch debate 23.5.2005 John Langford, Partha Niyogi, et al. John Langford, Partha Niyogi, Yann LeCun, Yasemin Altun, Zoubin Ghahramani, David McAllester, Sanjoy Dasgupta	148 views, 25:46 Lunch debate 24.5.2005Lunch debate 24.5.2005 John Langford, Robert Schapire, et al. John Langford, Robert Schapire, Yann LeCun, Yoram Singer, Mikhail Belkin	93 views, 35:40 Lunch debate 25.5.2005Lunch debate 25.5.2005 John Langford, Robert Schapire, et al. John Langford, Robert Schapire, Yann LeCun, Yoram Singer, Yasemin Altun, David McAllester	58 views, 14:39 Lunch debate 27.5.2005Lunch debate 27.5.2005 Rich Caruana, John Langford, et al. Rich Caruana, John Langford, Mikhail Belkin, Adam Kalai