Kernels and Gaussian Processes

author: Mark Girolami, University of Glasgow

Categories

Top: Computer Science: Machine Learning: Kernel Methods
Top: Computer Science: Machine Learning: Gaussian Processes

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*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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