About
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)
Related categories
Uploaded videos:
Lectures
53:45
Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Late...
Feb 25, 2007
·
10494 Views
51:40
Condition numbers, regularisation and uncertainty principles of linear algebraic...
Feb 25, 2007
·
4123 Views
51:42
Numerical Methods for Solving Least Squares Problems with Constraints
Feb 25, 2007
·
21357 Views
52:57
On serial architectures for multiple classifier systems
Feb 25, 2007
·
3606 Views
01:03:50
Multi-stream modeling with applications in speech and multimodal processing
Feb 25, 2007
·
3519 Views
01:00:00
Nonparametric Bayesian Models in Machine Learning
Feb 25, 2007
·
19898 Views
01:02:56
Applications of Bayesian Sensitivity and Uncertainty Analysis to the Statistical...
Feb 25, 2007
·
5435 Views
56:24
Machine Learning, Uncertain Information, and the Inevitability of Negative `Prob...
Feb 25, 2007
·
7387 Views
37:26
Probabilistic user interfaces
Feb 25, 2007
·
5032 Views
01:04:22
Tractable Inference for Probabilistic Models by Free Energy Approximations
Feb 25, 2007
·
4516 Views
30:25
Redundant Bit Vectors for Searching High-Dimensional Regions
Feb 25, 2007
·
3567 Views
40:07
Language Models for Information Retrieval
Feb 25, 2007
·
7573 Views