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The Full Experience starts March 17, 2014
Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.
This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!
This course will teach you probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.
At the end of the course, you will leverage what you learned by solving the problem of a runaway robot that you must chase and hunt down!
You should either already know Python, or have enough experience with another language to be confident you can pick up what you need on your own. Fortunately, Python was built to be easy to learn, read, and use. If you already know another programming language, you'll be coding in Python in less than an hour. Check out Udacity's Introductory CS class (in Python) if you'd like some review. Additionally, knowledge of probability and linear algebra will be helpful but not required.
Tracking other cars with Kalman filters
Determining where to drive with A* search
Controlling steering and speeds with PID
Programming a self-driving car
Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.