Artificial Intelligence - Berkeley (Spring 2016)

About This Course

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

Prerequisites

CS 61A or 61B: Prior computer programming experience is expected (see below); most students will have taken both these courses.

CS 70 or Math 55: Facility with basic concepts of propositional logic and probability are expected (see below); CS 70 is the better choice for this course.

This course has substantial elements of both programming and mathematics, because these elements are central to modern AI. You should be prepared to review basic probability on your own if it is not fresh in your head. You should also be very comfortable programming on the level of CS 61B even though it is not strictly required.

CS61A AND CS61B AND CS70 is the recommended background.

Instructors

Pieter Abbeel

Pieter Abbeel (PhD Stanford, MS/BS KU Leuven) joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in 2008. He regularly teaches CS188: Introduction to Artificial Intelligence and CS287: Advanced Robotics. His research focuses on robot learning. Some results include machine learning algorithms which have enabled advanced helicopter aerobatics, including maneuvers such as tic-tocs, chaos and auto-rotation, which only exceptional human pilots can perform, and the first end-to-end completion of reliably picking up a crumpled laundry article and folding it. Academic honors include best paper awards at ICML and ICRA, the Sloan Fellowship, the Air Force Office of Scientific Research Young Investigator Program (AFOSR-YIP) award, the Okawa Foundation award, the MIT TR35, the IEEE Robotics and Automation Society (RAS) Early Career Award, and the Dick Volz award for best PhD thesis in robotics and automation.

Anca Dragan

I am a new Assistant Professor in the EECS Department at UC Berkeley. I am starting the InterACT Lab: Interactive Autonomy and Collaborative Technologies. Previously, I was a PhD student at Carnegie Mellon's Robotics Institute and a member of the Personal Robotics Lab. My research interests are in algorithmic human-robot interaction, and lie at the intersection of robotics, machine learning, and HCI. My goal is to enable robots to work with and around people, and I employ techniques from optimal control, manipulation, Bayesian inference, and cognitive science to do so.

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BerkeleyX: CS188x-SP16 Artificial Intelligence - Berkeley (Spring 2016)