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b232
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be5b33kui
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2024/05/27 14:40 xposik [Final exam]
2024/05/27 14:39 xposik [Mid-term exam assignments]
2024/05/27 14:39 xposik [Final exam]
2024/05/27 11:59 xposik [BE5B33KUI: Cybernetics and Artificial Intelligence]
2024/05/27 10:11 xposik [Final exam]
2024/02/16 15:12 dantuswa
2024/02/13 10:42 xposik [BE5B33KUI: Cybernetics and Artificial Intelligence]
2024/02/13 10:41 xposik [BE5B33KUI: Cybernetics and Artificial Intelligence]
2024/02/13 10:39 xposik
2023/02/17 07:36 external edit
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2024/05/27 14:40 xposik [Final exam]
2024/05/27 14:39 xposik [Mid-term exam assignments]
2024/05/27 14:39 xposik [Final exam]
2024/05/27 11:59 xposik [BE5B33KUI: Cybernetics and Artificial Intelligence]
2024/05/27 10:11 xposik [Final exam]
2024/02/16 15:12 dantuswa
2024/02/13 10:42 xposik [BE5B33KUI: Cybernetics and Artificial Intelligence]
2024/02/13 10:41 xposik [BE5B33KUI: Cybernetics and Artificial Intelligence]
2024/02/13 10:39 xposik
2023/02/17 07:36 external edit
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Next revision
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courses:be5b33kui:start [2024/02/13 10:41]
xposik
[BE5B33KUI: Cybernetics and Artificial Intelligence]
courses:be5b33kui:start [2024/05/27 11:59]
xposik
[BE5B33KUI: Cybernetics and Artificial Intelligence]
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====== BE5B33KUI: Cybernetics and Artificial Intelligence ======
====== BE5B33KUI: Cybernetics and Artificial Intelligence ======
-
Lectures
: [[https://petrposik.github.io|Petr Pošík]], [[http://cmp.felk.cvut.cz/~svoboda|Tomas Svoboda]]
+
Lecturers
: [[https://petrposik.github.io|Petr Pošík]], [[http://cmp.felk.cvut.cz/~svoboda|Tomas Svoboda]]
-
Labs
: [[https://mrs.felk.cvut.cz/members/
research-fellows
/swati-dantu|Swati Dantu]]
+
Lab instructors
: [[https://mrs.felk.cvut.cz/members/
phdstudents
/swati-dantu|Swati Dantu]]
The course introduces the students to the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transitions. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algorithms in computer labs.
The course introduces the students to the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transitions. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algorithms in computer labs.
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* [[courses:be5b33kui:labs:start|]]
* [[courses:be5b33kui:labs:start|]]
* [[courses:be5b33kui:literature|]]
* [[courses:be5b33kui:literature|]]
-
* [[https://
www
.fel.cvut.cz/cz/education/rozvrhy-ng.
B222
/public/html/predmety/43/58/p4358106.html|schedule
2022/
2023]]
+
* [[https://
intranet
.fel.cvut.cz/cz/education/rozvrhy-ng.
B232
/public/html/predmety/43/58/p4358106.html|schedule 2023
/2024
]]
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==== Final exam ====
==== Final exam ====
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+
<WRAP center round tip 60%>
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When preparing for the exam, you can also use this [[https://docs.google.com/presentation/d/1WumcPGnL4-Ig71ncIMITjh2ZxsTa8lcUeLl-3v7IQeo/edit?usp=sharing|presentation]] prepared by K. Poláková!
+
</WRAP>
+
The final exam is worth 40 points. You need at least 18 points from the final exam to pass.
The final exam is worth 40 points. You need at least 18 points from the final exam to pass.
courses/be5b33kui/start.txt
· Last modified: 2024/05/27 14:40 by
xposik