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CS 320 - Introduction to Artificial Intelligence
Foundations and main methods of Artificial Intelligence. Problem characteristics and spaces. Search and optimization techniques with a focus on uninformed and heuristic algorithms. Two player games and constraint satisfaction. Modelling and simulation. Comparison of logic-based, fuzzy, and probabilistic reasoning and knowledge representation methodologies. Machine learning: learning tasks, inductive learning, statistical-based learning, over-fitting, accuracy. *Prerequisite: CS 210, STAT 160 or 200, and MATH 221* Students cannot receive credit for both CS 320 and ENSE 411 (ENSE 496AC). 3.000 Credit hours 3.000 Lecture hours Levels: Undergraduate Schedule Types: Lecture, Examination Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Undergraduate Graduate

CS 320 - Introduction to Artificial Intelligence

Foundations and main methods of Artificial Intelligence. Problem characteristics and spaces. Search and optimization techniques with a focus on uninformed and heuristic algorithms. Two player games and constraint satisfaction. Modelling and simulation. Comparison of logic-based, fuzzy, and probabilistic reasoning and knowledge representation methodologies. Machine learning: learning tasks, inductive learning, statistical-based learning, over-fitting, accuracy. ***Prerequisite: CS 210, STAT 160 or 200, and MATH 221*** *Students cannot receive credit for both CS 320 and ENSE 411 (ENSE 496AC).*
3.000 Credit hours
3.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture, Examination

Computer Science Department

Restrictions:
Must be enrolled in one of the following Levels:
Undergraduate
Graduate

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