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Gaussian Process Basics
Published on Feb 25, 2007211451 Views
How on earth can a plain old Gaussian distribution be useful for sophisticated regression and machine learning tasks?
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Chapter list
Gaussian Process Basics00:02
Nonlinear regression with neural networks00:55
References01:24
Motivation: Machine learning02:00
Can all this be done with a plain old Gaussian distribution?03:00
Two-dimensional Gaussian03:39
Inference07:03
Inference0107:26
Another representation08:32
Another representation0111:28
Another representation0211:45
Aha!13:35
Gaussian quiz17:25
How do we build Gaussian distribution?24:08
How the matrix was made25:25
Extend to more points27:32
A Gaussian process29:47
Effect of hyperparameters34:09
Inference of hyperparameters37:03
Two-dimensional input space48:01
Efficient computation (... well, modestly efficient)50:09
Key computational requirements52:45
Choosing covariance functions54:15
\"Squared exponential\"55:51
Emulate infinite neural netwoks56:10
GPs for classification56:29
Connection to standard neural networks58:19
Gaussian Quiz solutions01:00:17
Gaussian processes compared with state-of-the-art nonlinear parametric models01:00:33