CS 461 Artificial Intelligence
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This course will introduce the fundamental ideas and techniques underlying the design of intelligent systems. Topics to be covered: Search methods, optimality and heuristics for problem solving. Probability theory, decision-making in fully informed, partially observable and adversarial settings. Introductory level: Learning via interaction with the environment (reinforcement learning). Learning from data (machine/deep learning). Graphical models and causality. Application domains of the (state-of-the-art) methods.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
(CS 102 or CS 114 or CS 115) and (MATH 225 or MATH 220 or MATH 224 or MATH 241) and (MATH 230 or MATH 255 or MATH 260).
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