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Contacts: Matej Hoffmann, Tomas Svoboda, Filipe Gama
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 expects some basics of probability and linear algebra to be known to students. We expect that students are able to write decent computer programs in a higher level language (Java, Python), and have basic knowledge about data structures. Python and Matlab will be used in computer labs.
The final assesment will be composed of thee components:
45 points for homework (assignments during comp. labs)
Note: There will be some bonus points for discussions/quizzes during the computer labs.
The final exam is worth 40 points. Minimum for a non-F grade is 15 points from the final exam.
Note that that you will need to fulfil the criteria specified above - collect points for assignments during the semester. Otherwise, you will not be allowed to take the final exam.
The first exam date will be It will be a distance exam, subject to the following rules. The first exam date is Monday, June 8, 12:45-15:00. You will receive an invitation to the BBB conference room. We will explain all the details and at 1pm, you can start the online exam. You will need a laptop camera on during the exam. Additional dates will be set up later. At least one is planned toward the end of June. If you have specific constraints, please let us know in advance.
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.
F means fail.