This course will provide a broad overview and understanding of the underpinnings of established artificial intelligence techniques. In addition, we will develop a simple AI algorithm for one or two specific problems as a class if time permits.

Perspectives and techniques in artificial intelligence. Depending on interest, we will cover basic algorithms in one or more of the following areas: motion planning, natural language processing, vision, classification.

Basic methods and approaches in AI and their applications. Specific topics that we will discuss will be determined by in-class interest. Topics can include the following: vision, motion planning, classifiers (Naive Bayes or Decision Trees), search space formulation.

Exposure to the modern paradigms of AI and some of the problems that AI researchers are tackling. One simple but powerful model will be discussed (Bayes nets).

Learn how AI works at a high level and it's limitations. If interest permits, specific algorithms will be discussed.

Overview of the field of AI. Some basic algorithms and concepts such as searching, vision, and logical deduction.

Topics include an overview of Robert Lang's circle-river packing (tree method), tessellation, and point splits. Origami diagramming notation and basic folds will be explained for those with no exposure.