The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. This course is an introduction to the field, with special emphasis on sound modern methods. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, machine learning (decision trees, neural nets, reinforcement learning, and genetic algorithms) and machine vision. Programming exercises will concretize the key methods. The course targets graduate students and advanced undergraduates. Evaluation is based on programming assignments, a midterm exam, and a final exam.
If you are unsure about any of these, please speak with the instructor.
Please post all questions to Piazza!
You can find autograder information here.
This course is based on the CS 188 course at UC Berkeley. You may find lectures, slides, and more there.
The required book for this course is Artificial Intelligence: A Modern Approach byEither the second or third edition is fine. This is a classic textbook and highly recommended!
Another good reference for reinforcement learning is Reinforcement Learning: An Introduction byThe book is available online here.