Learning with Gaussian Processes
author:
Carl Edward Rasmussen,
Max Planck Institute for Biological Cybernetics
Description
This presentation describes the basic foundations and advanced theory of Gaussian processes.
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| Slides | |
| 0:00 | Learning with Gaussian Processes |
| 4:07 | Supervised Learning: The Prediction Problem |
| 5:16 | Outline |
| 5:46 | The Gaussian Distribution |
| 7:41 | Conditionals and Marginals of a Gaussian |
| 9:30 | What is a Gaussian Process? |
| 12:52 | The Marginalization Property |
| 15:28 | Random Functions from a Gaussian Process |
| 19:32 | Some Values of the Random Function |
| 20:30 | Random Functions from a Gaussian Process |
| 21:05 | Some Values of the Random Function |
| 21:22 | Sequential Generation |
| 29:23 | - Questions |
| 34:14 | Maximum Likelihood, Parametric Model |
| 39:34 | Bayesian Inference, Parametric Model |
| 43:10 | Bayesian Inference, Parametric Model, cont. |
| 46:48 | Non-Parametric Gaussian Process Models |
| 52:31 | Prior and Posterior |
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This is an awesome lecture. I love it. I learned a lot about probability distributions over functions.
Excellent lecture indeed! Highly recommended for those who want to learn about gaussian process. Well, I think I will create an account here to put yet another star for this video lecture. Thanks, Prof. Rasmussen!