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The goal of this subject is to introduce the basics of artificial intelligence. We will cover the algorithms of informed and uninformed state space, problem solving methods, reinforcement learning, knowledge representation and (sequential) decision making under uncertainty. We will avoid most of classical machine learning, since that is the focus of other courses.
Lectures:
Viliam Lisý , Branislav Bošanský
Seminars:
Petr Tomášek , Ondřej Kubíček , Karolina Drabent , Jan Mrkos , Xuzhe Dang
Time tables: Czech English
BRUTE - homework submissions
Labs Notebooks (Google drive, use FEL Google Account)
The students can gain at most 30 points for homework assignments. In order to get the credit (zápočet), they have to submit each task for at least 5 points (before the penalisation for late submissions - penalisation does not prevent getting the credit) and gain at least 15 points for home works overall.
Penalisation for late submission:
Always work on your assignments individually. Plagiarism is being detected and it is not tolerated. If you have an objective reason for difficulties with finishing the assignment on time, contact us ASAP, please.
In the middle of the semester, there will be a test similar to the final exam for 15 points.
The final exam is for up to 55 points:
Example past exam tests will be published. Howevre, since the topics we talk about gradually change, they may be outdated.
Exam dates:
Example of the questions for midterm test:
More details on chapters and links to other resources are in the slides.