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] 141 views, 35:49 WelcomeWelcome David McAllester, John Langford David McAllester, John Langford	239 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] 4667 views, 1:37:46 Online Learning and Game TheoryOnline Learning and Game Theory Adam Kalai Adam Kalai 3 comments	[syn] 363 views, 1:42:36 Generalization boundsGeneralization bounds John Langford John Langford	[syn] 1538 views, 1:00:56 Introduction to Kernel MethodsIntroduction to Kernel Methods Mikhail Belkin Mikhail Belkin	1307 views, 47:32 Semi-supervised Learning, Manifold MethodsSemi-supervised Learning, Manifold Methods Mikhail Belkin Mikhail Belkin	2104 views, 1:21:57 Introduction to Kernel MethodsIntroduction to Kernel Methods Partha Niyogi Partha Niyogi 5 comments
818 views, 54:52 Semi-supervised Learning, Manifold MethodsSemi-supervised Learning, Manifold Methods Partha Niyogi Partha Niyogi 1 comment	[syn] 164 views, 1:04:59 Evidence Integration in BioinformaticsEvidence Integration in Bioinformatics Phil Long Phil Long	219 views, 2:01:19 Empirical Comparisons of Learning Methods & Case StudiesEmpirical Comparisons of Learning Methods & Case Studies Rich Caruana Rich Caruana	[syn] 9446 views, 2:07:12 BoostingBoosting Robert Schapire Robert Schapire 9 comments	[syn] 995 views, 1:50:23 Trees for Regression and ClassificationTrees for Regression and Classification Robert Nowak Robert Nowak
124 views, 49:49 Algorithms for Learning and their EstimatesAlgorithms for Learning and their Estimates Steve Smale Steve Smale	650 views, 3:38:44 Energy-based models & Learning for Invariant Image RecognitionEnergy-based models & Learning for Invariant Image Recognition Yann LeCun Yann LeCun	426 views, 1:23:27 Online Learning with KernelsOnline Learning with Kernels Yoram Singer Yoram Singer 1 comment	119 views, 53:58 Fingerprints of Rhthm in Natural LanguageFingerprints of Rhthm in Natural Language Antonio Galves Antonio Galves	1072 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
268 views, 50:51 Learning to SignalLearning to Signal Brian Skyrms Brian Skyrms	[syn] 4795 views, 32:36 The Dynamics of AdaBoostThe Dynamics of AdaBoost Cynthia Rudin Cynthia Rudin 3 comments	83 views, 53:01 Learning variable covariances via gradientsLearning variable covariances via gradients Ding-Xuan Zhou Ding-Xuan Zhou	101 views, 1:05:34 Feasible Language LearningFeasible Language Learning Ed Stabler Ed Stabler	134 views, 1:10:31 On the evolution of languagesOn the evolution of languages Felipe Cucker Felipe Cucker
[syn] 147 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] 180 views, 56:08 Some Aspects of Learning Rates for SVMsSome Aspects of Learning Rates for SVMs Ingo Steinwart Ingo Steinwart	206 views, 51:40 Categorical Perception + Linear Learning = Shared CultureCategorical Perception + Linear Learning = Shared Culture Mark Liberman Mark Liberman	186 views, 58:48 Multiscale analysis on graphsMultiscale analysis on graphs Mauro Maggioni Mauro Maggioni	[syn] 82 views, 53:48 Adventures with CamilleAdventures with Camille Peter Culicover Peter Culicover
128 views, 51:50 On Optimal Estimators in Learning TheoryOn Optimal Estimators in Learning Theory Vladimir Temlyakov Vladimir Temlyakov	[syn] 1626 views, 2:38:48 Tutorial on Machine Learning ReductionsTutorial on Machine Learning Reductions John Langford John Langford 3 comments	[syn] 4530 views, 1:35:21 Information GeometryInformation Geometry Sanjoy Dasgupta Sanjoy Dasgupta 3 comments	[syn] 2401 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	6444 views, 1:24:46 Bayesian LearningBayesian Learning Zoubin Ghahramani Zoubin Ghahramani 9 comments
116 views, 1:44:36 Learning on Structured DataLearning on Structured Data David McAllester David McAllester 1 comment	1187 views, 1:16:33 Learning on Structured DataLearning on Structured Data Yasemin Altun Yasemin Altun 1 comment	648 views, 1:24:18 An introduction to grammars and parsingAn introduction to grammars and parsing Mark Johnson Mark Johnson 3 comments	73 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
624 views, 04:16 Short interviews MLSS05 Chicago by John LangfordShort interviews MLSS05 Chicago by John Langford John Langford John Langford
Debates
354 views, 1:23:45 Lunch debate 23.5.2005Lunch debate 23.5.2005 John Langford, David McAllester, et al. John Langford, David McAllester, Yasemin Altun, Yann LeCun, Zoubin Ghahramani, Sanjoy Dasgupta, Partha Niyogi	155 views, 25:46 Lunch debate 24.5.2005Lunch debate 24.5.2005 John Langford, Robert Schapire, et al. John Langford, Robert Schapire, Yann LeCun, Mikhail Belkin, Yoram Singer	97 views, 35:40 Lunch debate 25.5.2005Lunch debate 25.5.2005 John Langford, Yasemin Altun, et al. John Langford, Yasemin Altun, Yann LeCun, Yoram Singer, Robert Schapire, David McAllester	63 views, 14:39 Lunch debate 27.5.2005Lunch debate 27.5.2005 John Langford, Rich Caruana, et al. John Langford, Rich Caruana, Mikhail Belkin, Adam Kalai