Machine Learning Workshop, Sheffield 2004
12 Lectures · Sep 6, 2004
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Machine Learning is the study of computer algorithms that improve automatically through experience. Applications range from datamining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests. (Machine Learning, Tom Mitchell, McGraw Hill, 1997)
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Lectures
53:45
Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Late...
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Condition numbers, regularisation and uncertainty principles of linear algebraic...
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Numerical Methods for Solving Least Squares Problems with Constraints
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On serial architectures for multiple classifier systems
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01:03:50
Multi-stream modeling with applications in speech and multimodal processing
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Nonparametric Bayesian Models in Machine Learning
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Applications of Bayesian Sensitivity and Uncertainty Analysis to the Statistical...
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Machine Learning, Uncertain Information, and the Inevitability of Negative `Prob...
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37:26
Probabilistic user interfaces
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Tractable Inference for Probabilistic Models by Free Energy Approximations
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30:25
Redundant Bit Vectors for Searching High-Dimensional Regions
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40:07
Language Models for Information Retrieval
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