Machine Learning Summer School (MLSS), Chicago 2005
40 Lectures · May 15, 2005
About
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.
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Uploaded videos:
Introduction
Welcome
Apr 19, 2007
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4116 Views
Welcome to Chicago, and a (brief!) introduction to machine learning
Feb 25, 2007
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5869 Views
Lectures
Online Learning and Game Theory
Feb 25, 2007
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28927 Views
Generalization bounds
Feb 25, 2007
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8647 Views
Introduction to Kernel Methods
Feb 25, 2007
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14744 Views
Semi-supervised Learning, Manifold Methods
Feb 25, 2007
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16441 Views
Introduction to Kernel Methods
Feb 25, 2007
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17870 Views
Semi-supervised Learning, Manifold Methods
Feb 25, 2007
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9187 Views
Evidence Integration in Bioinformatics
Feb 25, 2007
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5136 Views
Empirical Comparisons of Learning Methods & Case Studies
Feb 25, 2007
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6163 Views
Trees for Regression and Classification
Feb 25, 2007
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10448 Views
Algorithms for Learning and their Estimates
Feb 25, 2007
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3789 Views
Energy-based models & Learning for Invariant Image Recognition
Feb 25, 2007
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13319 Views
Online Learning with Kernels
Feb 25, 2007
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7099 Views
Fingerprints of Rhthm in Natural Language
Feb 25, 2007
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3495 Views
Diffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical System...
Feb 25, 2007
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10905 Views
Learning to Signal
Feb 25, 2007
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3948 Views
The Dynamics of AdaBoost
Feb 25, 2007
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24719 Views
Learning variable covariances via gradients
Feb 25, 2007
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3741 Views
Feasible Language Learning
Feb 25, 2007
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3534 Views
On the evolution of languages
Feb 25, 2007
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3695 Views
Game Dynamics with Learning and Evolution of Universal Grammar
Feb 25, 2007
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3285 Views
Some Aspects of Learning Rates for SVMs
Feb 25, 2007
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5757 Views
Categorical Perception + Linear Learning = Shared Culture
Feb 25, 2007
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3520 Views
Multiscale analysis on graphs
Feb 25, 2007
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4625 Views
Adventures with Camille
Feb 25, 2007
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4345 Views
On Optimal Estimators in Learning Theory
Feb 25, 2007
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3635 Views
Tutorial on Machine Learning Reductions
Feb 25, 2007
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16463 Views
Information Geometry
Feb 25, 2007
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35534 Views
On the Borders of Statistics and Computer Science
Feb 25, 2007
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14032 Views
Bayesian Learning
Feb 25, 2007
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41428 Views
Learning on Structured Data
Feb 25, 2007
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3963 Views
Learning on Structured Data
Feb 25, 2007
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11776 Views
An introduction to grammars and parsing
Feb 25, 2007
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10487 Views
Learning patterns in omic data: applications of learning theory
Feb 25, 2007
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4460 Views
Interviews with students
Short interviews MLSS05 Chicago by John Langford
Feb 25, 2007
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6501 Views
Debates
Lunch debate 23.5.2005
Feb 25, 2007
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6867 Views
Lunch debate 24.5.2005
Feb 25, 2007
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5176 Views
Lunch debate 25.5.2005
Feb 25, 2007
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5463 Views
Lunch debate 27.5.2005
Feb 25, 2007
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3668 Views