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Gaussian Processes in Practice Workshop
Pascal

Gaussian Process Basics

author: David MacKay, University of Cambridge

Description

How on earth can a plain old Gaussian distribution be useful for sophisticated regression and machine learning tasks?

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Slides
0:02 Gaussian Process Basics
0:55 Nonlinear regression with neural networks
1:24 References
2:00 Motivation: Machine learning
3:00 Can all this be done with a plain old Gaussian distribution?
3:39 Two-dimensional Gaussian
7:03 Inference
7:26 Inference01
8:32 Another representation
11:28 Another representation01
11:45 Another representation02
13:35 Aha!
17:25 Gaussian quiz
24:08 How do we build Gaussian distribution?
25:25 How the matrix was made
27:32 Extend to more points
29:47 A Gaussian process
34:09 Effect of hyperparameters
37:03 Inference of hyperparameters
48:01 Two-dimensional input space
50:09 Efficient computation (... well, modestly efficient)
52:45 Key computational requirements
54:15 Choosing covariance functions
55:51 \"Squared exponential\"
56:10 Emulate infinite neural netwoks
56:29 GPs for classification
58:19 Connection to standard neural networks
60:17 Gaussian Quiz solutions
60:33 Gaussian processes compared with state-of-the-art nonlinear parametric models

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