Time and Place
Tue, Thu: 2:55pm to 4:10pm
Place: TBD.
Instructor
Syllabus
We study the problem of how an agent can learn to perceive its world well enough to act in it, to make reliable plans, and to learn from its own experience. The focus will be on algorithms and machine learning techniques for autonomous operation of robots. The course has a term project (teams with 2-3 students) involving physical robots.
Topics include: Markov decision process; Filtering and state estimation (Kalman filters, particle filters); Learning (reinforcement and supervised learning); Planning and control; Perception (vision, sensing). See syllabus.
Robotics
This course is for CS, ECE and MAE juniors, seniors and PhD students to teach them algorithms for robotic applications.
Machine Learning
Large parts of the course teach machine learning and artificial intelligence methods and techniques.
Why do I need this?
In recent years, several off-the-shelf robots have become available and some of them have made their way into our homes and offices. The ability to program robots has therefore become an important skill; e.g., for robotics research as well as in several companies (such as iRobot, Willow Garage, medical robotics, and others).