The course deepens and enriches knowledge of AI gained in the bachelor course Cybernetics and Artificial Intelligence. Students will get an overview of other methods used in AI, and will get a hands-on experience with some of them. They will master other required abilities to build intelligent agents. By applying new models, they will reiterate the basic principles of machine learning, techniques to evaluate models, and methods for overfitting prevention. They will learn about planning and scheduling tasks, and about methods used to solve them. The student will also get acquainted with the basics of probabilistic graphical models, Bayesian networks, and Markov models, and will learn their applications. Part of the course will introduce students to the area of again popular neural networks, with an emphasis on new methods for deep learning.
During the semester, including the exam, students can get at most 100 points: 40 points for the semestral projects and 60 points for the exam. To successfully pass the exam, students need to get at least 30 points out of 60, i.e. 50 %.
We shall use the usual grading table:
|Points||>=90||<80, 90)||<70, 80)||<60, 70)||<50, 60)||(0, 50)|