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Course description: 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 provides a basic introduction to key techniques: state-space search, game-playing, probabilistic reasoning, and learning. Over the last 50 years the field has grown tremendously, and this course can only scratch the surface within the field. This course begins with a discussion of what "artificial intelligence" means and how it can be useful. Next is a grounding in state-space search, problem formulation and solving. We then consider state-space search in the context of game playing, which then leads to alpha-beta pruning, 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.