Description: This class, taught by one of the foremost experts in AI, will teach you basic methods in Artificial Intelligence, including: probabilistic inference, computer vision, machine learning, and planning, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. You will get a chance to visit, via video, the leading research labs in the field, and meet the scientists and engineers who are building self-driving cars at Stanford and Google.
Prerequisites: The instructor will assume solid knowledge of programming, all programming will be in Python. Knowledge of probability and linear algebra will be helpful.
Basics of probability
Car localization with particle filters
Gaussians and continuous probability
Tracking other cars with Kalman filters
Image Processing and Machine Learning
Finding objects in sensor data
Planning and search
Determining where to drive with A* search
Finding optimal routes with dynamic programming
Controlling steering and speeds with PID
Putting it all together
Programming a self-driving car
Exam testing your knowledge