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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4115 Views
Welcome to Chicago, and a (brief!) introduction to machine learning
Feb 25, 2007
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5867 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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8644 Views
Introduction to Kernel Methods
Feb 25, 2007
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14743 Views
Semi-supervised Learning, Manifold Methods
Feb 25, 2007
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16438 Views
Introduction to Kernel Methods
Feb 25, 2007
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17867 Views
Semi-supervised Learning, Manifold Methods
Feb 25, 2007
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9186 Views
Evidence Integration in Bioinformatics
Feb 25, 2007
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5135 Views
Empirical Comparisons of Learning Methods & Case Studies
Feb 25, 2007
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6161 Views
Trees for Regression and Classification
Feb 25, 2007
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10445 Views
Algorithms for Learning and their Estimates
Feb 25, 2007
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3788 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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7098 Views
Fingerprints of Rhthm in Natural Language
Feb 25, 2007
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3494 Views
Diffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical System...
Feb 25, 2007
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10904 Views
Learning to Signal
Feb 25, 2007
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3946 Views
The Dynamics of AdaBoost
Feb 25, 2007
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24716 Views
Learning variable covariances via gradients
Feb 25, 2007
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3740 Views
Feasible Language Learning
Feb 25, 2007
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3532 Views
On the evolution of languages
Feb 25, 2007
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3693 Views
Game Dynamics with Learning and Evolution of Universal Grammar
Feb 25, 2007
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3283 Views
Some Aspects of Learning Rates for SVMs
Feb 25, 2007
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5756 Views
Categorical Perception + Linear Learning = Shared Culture
Feb 25, 2007
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3519 Views
Multiscale analysis on graphs
Feb 25, 2007
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4625 Views
Adventures with Camille
Feb 25, 2007
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4344 Views
On Optimal Estimators in Learning Theory
Feb 25, 2007
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3633 Views
Tutorial on Machine Learning Reductions
Feb 25, 2007
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16463 Views
Information Geometry
Feb 25, 2007
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35522 Views
On the Borders of Statistics and Computer Science
Feb 25, 2007
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14028 Views
Bayesian Learning
Feb 25, 2007
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41428 Views
Learning on Structured Data
Feb 25, 2007
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3962 Views
Learning on Structured Data
Feb 25, 2007
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11774 Views
An introduction to grammars and parsing
Feb 25, 2007
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10486 Views
Learning patterns in omic data: applications of learning theory
Feb 25, 2007
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4457 Views
Interviews with students
Short interviews MLSS05 Chicago by John Langford
Feb 25, 2007
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6500 Views
Debates
Lunch debate 23.5.2005
Feb 25, 2007
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6866 Views
Lunch debate 24.5.2005
Feb 25, 2007
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5174 Views
Lunch debate 25.5.2005
Feb 25, 2007
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5461 Views
Lunch debate 27.5.2005
Feb 25, 2007
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3667 Views