Course Description
CSE 415: Introduction to Artificial Intelligence The University of Washington, Seattle, Autumn 2017
Artificial intelligence is a broad field encompassing computational models of human cognition, formal systems for representing and processing symbolic information, expert systems technology, and a variety of techniques for learning, understanding signals, and solving problems.   This course begins with a discussion of what "artificial intelligence" means and how it can be useful. Then we explore the Python programming language and how it can be used to represent and process symbolic information. As an example, we focus on inheritance hierarchies and the reasoning that derives from the properties of a partial order. That's followed by an in-depth treatment of state-space search and problem formulation and solving. We then consider state-space search in the context of game playing, which then leads to expectimax search and the modeling of uncertainty. After that, we focus primarily on machine learning, starting with methods for reasoning under uncertainty. We cover learning of decision trees from examples, reinforcement learning in Markov Decision Processes, perceptron learning, and deep learning. We touch briefly on the future of AI.   The teaching methodology combines lectures, in-class and out-of-class exercises, and programming assignments.