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Contacts: Tomas Svoboda, Zdenek Straka, Matej Hoffmann

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 a state transition. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown, which also connects the artificial intelligence and cybernetics fields. 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 expected 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.

points: 50 for homework and 20 for the mid-term exam make together 70 points from the term work. The final exam is worth of 30 points.
*At least 35 points (out of 70) are needed before going to the final exam.* There will be some bonus points for discussions. Minimum for a non-F grade is 10 points from the final exam.

A | B | C | D | E | F |
---|---|---|---|---|---|

100-91 | 90-81 | 80-71 | 70-61 | 60-51 | 50-0 |

**F** means fail.

Wed 24 May, 14:30, KN:E-112 (1st floor E-building end of the corridor).

Written exam, 90 minutes. Closed book exam, essential equations will provided with the problem exposition.

courses/be5b33kui/start.txt ยท Last modified: 2018/02/05 18:01 by svobodat