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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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courses:be5b33kui:start [2024/05/27 10:11]
xposik
[Final exam]
courses:be5b33kui:start [2024/05/27 14:39]
xposik
[Final exam]
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====== BE5B33KUI: Cybernetics and Artificial Intelligence ======
====== BE5B33KUI: Cybernetics and Artificial Intelligence ======
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Lectures
: [[https://petrposik.github.io|Petr Pošík]], [[http://cmp.felk.cvut.cz/~svoboda|Tomas Svoboda]]
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Lecturers
: [[https://petrposik.github.io|Petr Pošík]], [[http://cmp.felk.cvut.cz/~svoboda|Tomas Svoboda]]
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Labs
: [[https://mrs.felk.cvut.cz/members/phdstudents/swati-dantu|Swati Dantu]]
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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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==== Final exam ====
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==== Final exam ====
9
<WRAP center round tip 60%>
<WRAP center round tip 60%>
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It will be a written exam, lasting 120 min. No books or notes will be allowed. The necessary formulas will be provided. The questions will be rather practical - you will be asked to solve (calculate) particular problems, similar to those during the lectures and labs.
It will be a written exam, lasting 120 min. No books or notes will be allowed. The necessary formulas will be provided. The questions will be rather practical - you will be asked to solve (calculate) particular problems, similar to those during the lectures and labs.
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Exam dates:
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Exam dates
for Spring 2024
:
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*
TBD (regular)
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*
Presence exam: Friday, May 31, 10:00, KN:E-128
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*
TBD
(re-take)
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*
Presence exam: Wednesday, June 5, 10:00, KN:E-128
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* Online exam for EuroTeQ students: Thursday, June 6, 16:00, BBB room
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* Presence exam
(re-take)
: Monday, June 17, 9:00, KN:E-301
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* Online exam for EuroTeQ students: TBA
courses/be5b33kui/start.txt
· Last modified: 2024/05/27 14:40 by
xposik