Probability, Information Theory and Bayesian Inference
author:
Joaquin Quiñonero Candela,
Max Planck Institute for Biological Cybernetics
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| Slides | |
| 0:00 | - Probabilistic Machine Learning - Announcement |
| 0:22 | Probabilistic Machine Learning |
| 3:51 | Why Probabilistic Models for Learning? |
| 7:20 | Probabilities and Ensembles |
| 10:59 | Basic Rules of Probability |
| 14:57 | Expectation and Variance (Moments) |
| 18:58 | Example of Joint Probability - Bigrams |
| 22:54 | An Exercise on Mammographies |
| 25:41 | Solving the Mammography Exercise Writing Down Probabilities |
| 27:16 | An Exercise on Mammographies |
| 27:22 | Solving the Mammography Exercise Writing Down Probabilities |
| 28:46 | Solving the Mammography Exercise Playing with Concrete Numbers |
| 30:31 | Solving the Mammography Exercise Apply Bayes’ Rule |
| 33:04 | Do You Trust Your Doctor? |
| 36:12 | Information, Probability and Entropy |
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This is a very good basic introductionary lecture. it is very well presented and understandable.
it makes heavy use of David MacKays book which can be found and read online on his website: http://www.inference.phy.cam.ac.uk/ma...
a second book that is only mentioned in passing is jaynes book: "Propability Theory: the logic of science" of which the first three chapters are available here: http://bayes.wustl.edu/etj/prob/book.pdf
Well presented and well recorded.