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).
Autumn Semester (Özgür Salih Öğüz)
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