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be5b33kui
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2017/03/06 12:27 hoffmmat
2017/03/06 12:27 hoffmmat adding slides - Matej
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courses:be5b33kui:lectures:start [2017/05/11 12:23]
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courses:be5b33kui:lectures:start [2018/02/18 15:41]
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^ date ^ week ^ topic ^
^ date ^ week ^ topic ^
| 20.02.2017 | 1. | Intro into course and cybernetics and AI. We learn about the course grading and its rules as well as about what connects the cybernetics and artificial intelligence. I show some real applications of the methods we learn during the courses in an attempt to make the first lecture not too much dry and boring. {{:courses:be5b33kui:lectures:kui-01-intro.pdf|}}, {{:courses:be5b33kui:lectures:chapter01.pdf|}}|
| 20.02.2017 | 1. | Intro into course and cybernetics and AI. We learn about the course grading and its rules as well as about what connects the cybernetics and artificial intelligence. I show some real applications of the methods we learn during the courses in an attempt to make the first lecture not too much dry and boring. {{:courses:be5b33kui:lectures:kui-01-intro.pdf|}}, {{:courses:be5b33kui:lectures:chapter01.pdf|}}|
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| 27.02.2017 | 2. | Solving problems by search. We learn how to formalize problems and how to design //algorithms// for finding a
{{:courses:be5b33kui:lectures:kui-06-mdp.pdf|}}
solution. {{:courses:be5b33kui:lectures:chapter03.pdf|}}, {{:courses:be5b33kui:lectures:kui-02-search.pdf|}} (not completed, continue on March 13) |
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| 27.02.2017 | 2. | Solving problems by search. We learn how to formalize problems and how to design //algorithms// for finding a solution. {{:courses:be5b33kui:lectures:chapter03.pdf|}}, {{:courses:be5b33kui:lectures:kui-02-search.pdf|}} (not completed, continue on March 13) |
| 06.03.2017 | 3. | {{:courses:be5b33kui:lectures:kui-03-embodiedai.pdf|Embodied artificial intelligence}}, a lecture by [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]] - a slight diversion into an interesting perspective on AI and robotics. |
| 06.03.2017 | 3. | {{:courses:be5b33kui:lectures:kui-03-embodiedai.pdf|Embodied artificial intelligence}}, a lecture by [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]] - a slight diversion into an interesting perspective on AI and robotics. |
| 13.03.2017 | 4. | Uniformed search, properties, comparisons. {{:courses:be5b33kui:lectures:kui-03-search.pdf|}}|
| 13.03.2017 | 4. | Uniformed search, properties, comparisons. {{:courses:be5b33kui:lectures:kui-03-search.pdf|}}|
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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|}} |
| 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 |
| <del>01.05.2017</del> | 11. ^ Public holiday |
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| **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|}} |
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| **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 |
| <del>08.05.2017</del> | 12. ^ Public holiday |
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| **11.05.2017** | 12. | Classification
– Perceptron, k
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nn and relationship to Bayesian classifier -
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| **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|}} |
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[[https://sites.google.com/site/matejhof/home|Matej Hoffmann]]
{{:courses:be5b33kui:lectures:kui-12-classification.pdf|}} |
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| 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.
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| 15.05.2017 | 13. | Learning probabilistic models - [[https://sites.google.com/site/matejhof/home|Matej Hoffmann]] |
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| 22.05.2017 | 14. |
Solving problems on paper/blackboard - fully interactive lecture; preparing for the exam a bit
|
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| 22.05.2017 | 14. |
ToBeDecided
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courses/be5b33kui/lectures/start.txt
· Last modified: 2018/02/18 15:41 by
svobodat