Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » PASCAL Bootcamp in Machine Learning, Vilanova 2007

Kernels and Gaussian Processes

author: Mark Girolami, University of Glasgow
published: July 9, 2007,   recorded: July 2007,   views: 1836
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Slides

Slides
0:00 Machine Learning
3:27 - Machine Learning - Lecture 2
4:02 Linear Regression - 1
5:41 Linear Regression - 2
6:22 Linear Regression - 3
6:33 Linear Regression - 4
6:59 Example Prediction Problem - 1
8:20 Example Prediction Problem - 2
8:34 Example Prediction Problem - 3
9:10 Example Prediction Problem - 4
9:40 Example Prediction Problem - 5
10:02 Example Prediction Problem - 6
11:27 Linear Model - 1
11:30 Linear Model - 2
11:39 Linear Model - 3
12:29 Loss Functions - 1
13:08 Loss Functions - 2
13:40 Loss Functions - 3
14:01 Squared-Error Loss - 1
14:17 Squared-Error Loss - 2
14:35 Squared-Error Loss - 3
14:48 Squared-Error Loss - 4
15:07 Matrix Notation - 1
16:04 Matrix Notation - 2
16:37 Squared-Error Loss - 5
16:41 Matrix Notation - 2
16:52 Squared-Error Loss - 6
17:39 Squared-Error Loss - 7
18:24 Minimising MSE - 1
18:30 Minimising MSE - 2
19:10 Stationary Point - 1
19:13 Stationary Point - 2
19:35 Stationary Point - 3
19:51 Stationary Point - 4
20:02 Stationary Point - 5
20:23 Stationary Point - 6
20:27 Stationary Point - 7
20:45 Stationary Point - 8
20:53 Stationary Point - 9
20:55 Stationary Point - 10
21:38 Stationary Point - 11
22:14 Stationary Point - 12
22:25 Stationary Point - 13
22:44 Stationary Point - 14
23:00 Least Squares Solution - 1
23:37 Stationary Point - 4
23:59 Least Squares Solution - 2
24:25 Least Squares Solution - 3
24:49 Least Squares Solution - 4
25:23 Stationary Point - 15
26:13 Prediction - 1
26:16 Prediction - 2
26:25 Prediction - 3
26:54 Prediction - 4
27:29 Prediction - 5
27:38 Nonlinear Model - 1
27:44 Prediction - 5
27:55 Stationary Point - 16
28:19 Nonlinear Model - 1
28:28 Nonlinear Model - 2
28:55 Nonlinear Model - 3
29:19 Nonlinear Model - 4
29:52 Nonlinear Model - 5
30:30 Nonlinear Model - 6
30:47 Nonlinear Model - 7
31:00 Nonlinear Model - 8
31:22 Nonlinear Model - 9
33:02 - Machine Learning - Lecture 5
33:47 Probabilistic Regression - 1
34:09 Probabilistic Regression - 2
34:11 Probabilistic Regression - 3
34:13 Probabilistic Regression - 4
34:54 Probabilistic Regression - 5
35:24 Probabilistic Regression - 6
35:35 Probabilistic Regression - 7
36:03 Noise Distribution - 1
37:30 Noise Distribution - 2
38:00 Noise Distribution - 3
38:48 Noise Distribution - 4
39:06 Probabilistic Regression - 8
39:44 Probabilistic Regression - 9
40:19 Probabilistic Regression - 10
40:35 Probabilistic Regression - 11
41:12 Probabilistic Regression - 12
41:19 Probabilistic Regression - 13
41:54 Probabilistic Regression - 14
42:33 Probabilistic Regression - 15
42:49 Probabilistic Regression - 16
43:22 Probabilistic Regression - 17
43:24 Probabilistic Regression - 18
44:22 Probabilistic Regression - 19
44:40 Maximum Likelihood - 1
44:45 Maximum Likelihood - 2
44:51 Maximum Likelihood - 3
45:09 Maximum Likelihood - 4
45:22 Maximum Likelihood - 5
46:14 Maximum Likelihood - 6
46:39 Maximum Likelihood - 7
47:03 Maximum Likelihood - 8
47:11 Maximum Likelihood - 9
47:36 Estimate Uncertainty - 1
48:03 Estimate Uncertainty - 2
48:33 Estimate Uncertainty - 3
48:51 Estimate Uncertainty - 4
48:57 Estimate Uncertainty - 5
49:17 Estimate Uncertainty - 6
50:12 Estimate Uncertainty - 7
53:04 Estimate Uncertainty - 8
53:32 Estimate Uncertainty - 9
54:06 Estimate Uncertainty - 10
54:21 Estimate Uncertainty - 11
54:47 Estimate Uncertainty - 12
55:53 Estimate Uncertainty - 13
56:15 Estimate Uncertainty - 14
56:48 Estimate Uncertainty - 15
56:52 Estimate Uncertainty - 16
58:58 Estimate Uncertainty - 17
59:16 Estimate Uncertainty - 18
59:46 Estimate Uncertainty - 19
61:54 Likelihood

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Reviews and comments:

Comment1 zslevi, July 28, 2009 at 7:48 p.m.:

I haven't watched the videos, but the slides are excellent. I've skimmed through a number of books, only to find the formulas for Gaussian prediction on the 5. lecture slides on the 51st page.

Comment2 ss, July 16, 2010 at 1:42 a.m.:

I don't understand why camera men in video lectures always tries to be creative and moves the camera around with no reason. Please, just capture the darned screen instead zooming out to the audience for no reasons!

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