Kernels and Gaussian Processes
author:
Mark Girolami,
University of Glasgow
Categories
Top: Computer Science: Machine Learning: Kernel MethodsTop: 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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