Undergraduate Courses

CSE 481c: Robotics Capstone Students work in teams to design and implement algorithms for robotic perception and control.
CSE 481O: Capstone Software - Kinect Students work in teams to design and implement a software project that makes use of RGB-D sensors (e.g. Microsoft Kinect, ASUS Xtion Pro Live)

Graduate Courses

CSE 571: Probabilistic Robotics This course introduces various techniques for Bayesian state estimation and its application to problems such as robot localization, mapping, and manipulation. The course will also provide a problem-oriented introduction to relevant machine learning and computer vision techniques.
CSE 579: Intelligent Control Through Learning &optimization Design or near-optimal controllers for complex dynamical systems, using analytical techniques, machine learning, and optimization. Topics from deterministic and stochastic optimal control, reinforcement learning and dynamic programming, numerical optimization in the context of control, and robotics. Prerequisite: vector calculus; linear algebra, and Matlab. Recommended: differential equations; stochastic processes, and optimization. Offered: jointly with AMATH 579.