By Jesse Spaulding from San Francisco, California 8 months ago Completed (Partially)

Taking this course from Sebastian was a pleasure. To start with he is exceedingly qualified to teach it. He was the director of Stanford's artificial intelligence lab and his team was the first to complete the 2005 DARPA grand challenge with their self driving car Stanley. He went on to lead the development of the Google self driving car.

In this course you'll learn the core algorithms that power Google's self driving car. And you'll learn directly from the source. "That's pretty cool!" as Sebastien would say. The passion and enthusiasm that Sebastien has for the subject comes across in the videos and is infectious.

The course is broken down into short video segments generally not more than 5 minutes. Plenty of quizzes and programming assignments are dispersed to keep you engaged and make sure you're learning.

Growing up I was a huge fan of Legos and my favorite thing happened to be building cars. I love machine learning, I love designing things, I love building things, and if you're like me you'll love this course.

My idea to make this course even cooler - create some kind of virtual world where students can deploy their own code to test in a simulated self driving environment. Perhaps even create a race or competition. Or alternatively, provide instructions for creating your own miniature self driving car for testing in your living room.

By Diego Allen from Asuncion, Asuncion 3 months ago Completed

Great course, maybe a follow up that goes more in-depth in some topics might be useful. Also virtual environments to help visualize how the different algorithms behave as Jesse Spaulding suggested in another comment would be a way to improve this course.

By Chad Colgur from Vancouver, British Columbia 5 months ago Completed

Prof. Thrun has a really engaging presentation style. It helps that he is one of the foremost accomplished practitioners in the field with an exhibit in the Smithsonian no less. You know you're learning from the best.

The course was almost exactly what I expected. I might have wanted a bit more "starter code" for my own project but that expectation seems naive in retrospect. Anyway, the problem solving techniques are the important bits. The rest is too project-specific for a remote learning environment.

It is difficult to imagine how this course could improve. Some people found the "utilitarian" approach to the mathematics a little distracting. You are asked to accept a few results on faith but that was never too difficult for me. I might have added a few links for the mathematically-inclined.

I haven't taken it but there is another Udacity course on mathematical modelling. It might be the one on differential equations. I did quite a bit of that in my classical education so it will probably be a while before I go back to it. Perhaps it is an alternative for you.

Some proficiency in Python would be useful for this course. I would also consider the Khan introduction to Linear Algebra, just so you are not seeing a matrix for the first time in this course. That said, the emphasis is on mastery at Udacity. You might want to use this course to drive your linear algebra learning.