A survey of essential Artificial Intelligence techniques and underlying theory. Basic search strategies, including uninformed search, heuristic search, and games. Basic knowledge representation and reasoning, including propositional satisfiability and theorem proving, Bayes rule, and Bayesian networks. Basic machine learning, including k-nearest neighbours, decision trees, neural networks, naive Bayes classifier, k-means. Weekly hours: 3 Lecture hours and 1 Tutorial hoursPrerequisite(s): MATH 163.3 or CMPT 260.3; and CMPT 280.3; and STAT 242.3, STAT 245.3 or equivalent (including EE 216 or ME 251). Note: Costs in addition to tuition apply to this course.
A survey of essential Artificial Intelligence techniques and underlying theory. Basic search strategies, including uninformed search, heuristic search, and games. Basic knowledge representation and reasoning, including propositional satisfiability and theorem proving, Bayes rule, and Bayesian networks. Basic machine learning, including k-nearest neighbours, decision trees, neural networks, naive Bayes classifier, k-means. Weekly hours: 3 Lecture hours and 1 Tutorial hoursPrerequisite(s): MATH 163.3 or CMPT 260.3; and CMPT 280.3; and STAT 242.3, STAT 245.3 or equivalent (including EE 216 or ME 251). Note: Costs in addition to tuition apply to this course.
