ITCS 6150: Foundations of Artificial Intelligence

University of North Carolina, Charlotte

Examines the basic design principles, concepts and algorithms that can be used to design artificially intelligent systems.�Topics include: �Agent models, search algorithms; game playing; constraint satisfaction problems; Markov decision processes & reinforcement learning; supervised learning; knowledge representation; logic; Bayesian & decision networks; sampling; advanced topics as time permits. Students are expected to have familiarity�with high-level, general-purpose programming language such as Python; Fundamental concepts from probability theory; Basic data structures and algorithms such as queues, trees, graphs, hash tables, and sorting.

Average GPA: 3.72

Instructors

• Dewan Ahmed 860 students, Average GPA 3.63
• Zbigniew Ras 528 students, Average GPA 3.68
• Ali Sever 357 students, Average GPA 3.97
• Jing Xiao 103 students, Average GPA 3.50
• Sterling Mcleod 70 students, Average GPA 3.83
• Agnieszka Dardzinska 39 students, Average GPA 4.00
• Atif Farid Mohammad 25 students, Average GPA 3.76