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!
Why Take This Course? 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!
### Lesson 1: Localization
- Total Probability
- Uniform Distribution
- Probability After Sense
- Normalize Distribution
- Phit and Pmiss
- Sum of Probabilities
- Sense Function
- Exact Motion
- Move Function
- Bayes Rule
- Theorem of Total Probability
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Pretty good course. I did not finish it because it overlapped a lot with the first version that Sebastian Thrun did together with Peter Norvig. From the first several units I got an impression that the course is an aggregation of loosely connected topics (as if the authors tried to cover a lot more than they had time for), but nevertheless each topic is well explained.