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This course is designed to complement (and deepen some of) the material presented in the bachelor-level course Cybernetics and Artificial Intelligence (KUI). Despite that, you can safely attend our course even without having finished KUI. Just be warned that the AI methods and techniques covered in KUI will not be part of UI course (especially graph search methods, basics of game theory, Markov decision processes, reinforcement learning), and your AI knowledge will not be complete without them!

You should be able to study this course if you have some background in the following:

  • probability theory (random events, conditional and joint probability, random variables and their distributions),
  • linear algebra (vectors, matrices, multiplication of vectors and matrices, dot product of two vectors, matrix inversion),
  • calculus (partial derivatives, gradient).

Background in optimization methods (linear and quadratic programming) is a plus, but not really required.

courses/ui/prerequisities.txt · Last modified: 2019/02/15 16:28 by xposik