Sep 6, 2004 
12 videos 
Machine Learning Workshop, Sheffield 2004
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)
Lectures
01:04:22
Tractable Inference for Probabilistic Models by Free Energy Approximations
Feb 25, 2007 4541 views
51:42
Numerical Methods for Solving Least Squares Problems with Constraints
Feb 25, 2007 21415 views
01:02:56
Applications of Bayesian Sensitivity and Uncertainty Analysis to the Statistical...
Feb 25, 2007 5458 views
01:00:00
Nonparametric Bayesian Models in Machine Learning
Feb 25, 2007 20038 views
51:40
Condition numbers, regularisation and uncertainty principles of linear algebraic...
Feb 25, 2007 4145 views
40:07
Language Models for Information Retrieval
Feb 25, 2007 7600 views
56:24
Machine Learning, Uncertain Information, and the Inevitability of Negative `Prob...
Feb 25, 2007 7471 views
52:57
On serial architectures for multiple classifier systems
Feb 25, 2007 3632 views
Multi-stream modeling with applications in speech and multimodal processing
Feb 25, 2007 3519 views
37:26
Probabilistic user interfaces
Feb 25, 2007 5057 views
53:45
Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Late...
Feb 25, 2007 10536 views
30:25
Redundant Bit Vectors for Searching High-Dimensional Regions
Feb 25, 2007 3598 views