Honestly, the class, "Programming a Robotic Car" sounded pretty lame. When am I going to program a robotic car? Never. As cool as it sounds in principle, I figured it would be pretty domain-specific and relevant only if you were doing autonomous robotics.
However, I must say that the syllabus looks amazing, and pretty hardcore. It appears to have less to do with robotic cars per se and more to do with applied controls and online machine learning. Particle filters? Kalman filters? Extracting signals from noisy sensor data? Shallow planning and search? In a way that's made applicable and not presented as just a series of proofs and theorems?
Honestly, the class, "Programming a Robotic Car" sounded pretty lame. When am I going to program a robotic car? Never.
Programming is not brick-laying. "Learning to program" is about learning to think and solve all kinds of problems, not learning now to do one specific thing that you are going to do for a job. You become a good programmer by learning about and solving all kinds of problems.
Programming a robotic car sounds incredibly awesome to me; hardly "lame", even though I never intend to work in robotics. In college, I took courses like "Filesystems" (no, I don't intend to write a filesystem), "Operating Systems" (no, I don't intend to be a Linux kernel dev), and so forth. Just because that's not what I'm doing doesn't make them interesting, useful, and fun.
A programmer who has only solved a very narrow range of problems -- and considers others s/he's not too familiar with to be "lame" -- is generally not a very good programmer.
I mean, I agree with you in principle. I think programming a robotic car would be really cool. There's a difference between a foundation (FS, OS, algorithms, etc) and lots of random specialist exposures, though.
I consider myself a generally good programmer. I've been doing it since I was ~6-7, so 15 years now, five of them professionally. I've solved problems in dozens of domains, using probably 20 languages in every major paradigm. But my background is in life sciences, and I've been increasingly feeling like checking out every "cool" technology for the consumption value is a great way to end up a dabbler. It's a little bit of a false dichotomoy, but not entirely. How does this matter to me? is a good first-order heuristic when you're already working 80 hours a week.
That said, I do know the fun of diving into things that seem interesting without necessarily having an immediate application -- I learned most of my languages and technologies that way -- but for some reason, "Programming a Robotic Car" I guess just didn't seem intrinsically interesting as stated. Maybe "I'm never going to use this" is the wrong rationalization for that reaction.
I'm pretty sure "programming a robotic car" involves solving all sorts of problems, not merely ones related to the fact that it is a car, and it is robotic.
Even in ordinary college CS, you're almost always faced with very specific assignments, like "write a memory allocator" or "write a search algorithm to help the cat find the mouse in this maze". The intent is to teach you a wide range of ideas and techniques; the specific goal of the task isn't really what's important.
Yeah, that's what I'm saying. The title made me think it would be specialist coverage, but it's actually widely-applicable tools just taught in the context of robotic cars. I'm pretty sure you could teach such a class either way.
The "Robotic Car" is nothing else but a way of attracting people to the course. Reading the contents of the course, we could easily substitute "Programming a Robotic Car" by "Artificial Intelligence for Robotics". I would like to see a bit more of Theory of Control on the topics but I guess it requires advanced Algebra.
Every time that I read about technology I feel like we have to realize that every time more generalists will be needed. Mechanics, dynamics, control, computer vision, energy, fluids, signal processing, artificial intelligence, statistics are some of the skills needed for a real Robotic Car, and of course, nobody can expect to learn them in 7 weeks.
My guess would be that the title of the class was aimed at students of ai-class.org, in which he touched on several of the topics you mention in the context of his work in automated driving. When I saw the title of the course it immediately struct me that this would be a deeper look into all the cool things he pointed out in ai-class.
However, I must say that the syllabus looks amazing, and pretty hardcore. It appears to have less to do with robotic cars per se and more to do with applied controls and online machine learning. Particle filters? Kalman filters? Extracting signals from noisy sensor data? Shallow planning and search? In a way that's made applicable and not presented as just a series of proofs and theorems?
I've signed up.