Artificial Intelligence

This course is a combined graduate/undergraduate level introduction to artifical intelligence course. Problem solving, search, and game trees. Knowledge representation, inference, and rule-based systems. Semantic networks, frames, and planning. Introduction to machine learning, neural-nets, and genetic algorithms. (Taught Fall 2019, Approx. 65 students).

Upon completion of this course, students will be able to:

• identify, formulate, analyze, and solve complex computing or engineering problems by applying principles of computing, engineering, science, and mathematics.
• design, implement, and evaluate a computing or engineering solution to meet a given set of requirements, with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
• develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

Graduate Section:

Course content developed by Dr. David Feil-Seifer and modified by Janelle Blankenburg (Spring 2019).