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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4112 Views
Welcome to Chicago, and a (brief!) introduction to machine learning
Feb 25, 2007
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5865 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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8641 Views
Introduction to Kernel Methods
Feb 25, 2007
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14741 Views
Semi-supervised Learning, Manifold Methods
Feb 25, 2007
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16429 Views
Introduction to Kernel Methods
Feb 25, 2007
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17866 Views
Semi-supervised Learning, Manifold Methods
Feb 25, 2007
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9183 Views
Evidence Integration in Bioinformatics
Feb 25, 2007
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5132 Views
Empirical Comparisons of Learning Methods & Case Studies
Feb 25, 2007
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6159 Views
Trees for Regression and Classification
Feb 25, 2007
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10443 Views
Algorithms for Learning and their Estimates
Feb 25, 2007
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3786 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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7097 Views
Fingerprints of Rhthm in Natural Language
Feb 25, 2007
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3493 Views
Diffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical System...
Feb 25, 2007
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10902 Views
Learning to Signal
Feb 25, 2007
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3945 Views
The Dynamics of AdaBoost
Feb 25, 2007
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24714 Views
Learning variable covariances via gradients
Feb 25, 2007
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3737 Views
Feasible Language Learning
Feb 25, 2007
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3531 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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3282 Views
Some Aspects of Learning Rates for SVMs
Feb 25, 2007
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5755 Views
Categorical Perception + Linear Learning = Shared Culture
Feb 25, 2007
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3518 Views
Multiscale analysis on graphs
Feb 25, 2007
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4625 Views
Adventures with Camille
Feb 25, 2007
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4343 Views
On Optimal Estimators in Learning Theory
Feb 25, 2007
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3632 Views
Tutorial on Machine Learning Reductions
Feb 25, 2007
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16463 Views
Information Geometry
Feb 25, 2007
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35507 Views
On the Borders of Statistics and Computer Science
Feb 25, 2007
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14027 Views
Bayesian Learning
Feb 25, 2007
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41428 Views
Learning on Structured Data
Feb 25, 2007
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3959 Views
Learning on Structured Data
Feb 25, 2007
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11773 Views
An introduction to grammars and parsing
Feb 25, 2007
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10484 Views
Learning patterns in omic data: applications of learning theory
Feb 25, 2007
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4455 Views
Interviews with students
Short interviews MLSS05 Chicago by John Langford
Feb 25, 2007
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6497 Views
Debates
Lunch debate 23.5.2005
Feb 25, 2007
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6864 Views
Lunch debate 24.5.2005
Feb 25, 2007
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5172 Views
Lunch debate 25.5.2005
Feb 25, 2007
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5460 Views
Lunch debate 27.5.2005
Feb 25, 2007
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3665 Views