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--- courses:be5b33kui:lectures:start [2017/05/11 12:23]
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+++ courses:be5b33kui:lectures:start [2017/05/19 16:14]
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 | 24.04.2017 |  10. | Reinforcement learning. What if nothing is known about the probability of action outcomes and we have to learn from final success or failure? {{:courses:be5b33kui:lectures:kui-08-rl.pdf|}} |
 | <del>01.05.2017</del> |  11. ^ Public holiday |
-| **02.05.2017** |  11. | Bayesian classification and decisions - [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]] . How to decide optimally if we know all the (conditional) probabilities. {{:courses:be5b33kui:lectures:kui-11-bayes.pdf|}} |
+| **02.05.2017** |  11. | Bayesian classification and decisions - [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]]. How to decide optimally if we know all the (conditional) probabilities. {{:courses:be5b33kui:lectures:kui-11-bayes.pdf|}} |
 | <del>08.05.2017</del> |  12. ^ Public holiday |
-| **11.05.2017** |  12. | Classification – Perceptron, k-nn and relationship to Bayesian classifier -
+| **11.05.2017** |  12. | Classification - [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]]. Perceptron, k-nn and relationship to Bayesian classifier - {{:courses:be5b33kui:lectures:kui-12-classification.pdf|}}   |
- [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]]  {{:courses:be5b33kui:lectures:kui-12-classification.pdf|}}   |
+| 15.05.2017 |  13. | Learning probabilistic models - [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]]. Slides from Jiri Matas: {{:courses:be5b33kui:lectures:Matas_pr_03_parameter_estimation_2016_10_17.pdf|}} - up to slide 15. |
-| 15.05.2017 |  13. | Learning probabilistic models - [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]] |
+| 22.05.2017 |  14. | Solving problems on paper/blackboard - fully interactive lecture; preparing for the exam a bit |
-| 22.05.2017 |  14. | ToBeDecided |