# used by Sebastian
    for k in range(len(measurements)):
        p = sense(p, measurements[k])

    # shorter, saner, and more Pythonic
    for m in measurements:
        p = sense(p, m)

  def move(p, U):
      q = []
      for i in range(len(p)):
          s = pExact * p[(i-U) % len(p)]
          s = s + pOvershoot * p[(i-U-1) % len(p)]
          s = s + pUndershoot * p[(i-U+1) % len(p)]
          q.append(s)
      return q

There's a more idiomatic way to get this index variable(http://stackoverflow.com/questions/522563/accessing-the-inde...), but using a consistent, easy to understand iteration method allows the poor confused student (i.e. me) to spend more time focusing on the concept being taught.

So the code could be intentional, or could be an accident. But I expect that sooner rather than later there will be some data to show which method, if any, is better for the students, and the classes will be adjusted accordingly.

By the way, did anyone else find these exercises addictive? I can begin to appreciate why the Farmville cow-clickers keep at it.

I am aware of two reasonable answers to the "how do I learn idiomatic style" question (in any language, although Python is the only language in which I personally have deliberately sought a better accent).

1) Read great codebases. For Python, the #1 target here is probably the standard library. People also often mention high-profile projects like Django or Twisted; since I haven't tried that I don't know what percentage of that code is beautiful, but I'm guessing it's probably a mix (so you'd have to apply some judgement).

2) The path I have taken is to read a lot of blogs and Stack Overflow questions and so on. Consider this: http://stackoverflow.com/search?q=pythonic In particular, answers by Alex Martelli are reliably very informative (even if I find his prose style a little sub-optimal).

3) Oh, and this is a bit abstract, but if you haven't typed 'import this' into your Python interpreter, you should do that, for some guidance about the abstract ethics of "being Pythonic".

I agree that simplicity of learning is a good goal. This is why I didn't raise the issue of the several dozen times where he uses an very ugly for loop where a very nice list comprehension would be the Pythonic thing to do. Being Pythonic is not the highest goal in software, I get that, and if you're trying to focus on learning robotics rather than learning Python, list comprehensions add friction.

But in the case of iteration across a list, using an idiomatic iterator is far from unique to python, and it's very readable to someone who has never seen it.

And I thought about the C/C++ thing, but the reason I said "Pascal programmer" rather than "C programmer" is because surely no-one trying to ease the translation to C would have used all those list.append() calls with which Sebastian is so liberal.

  # less readable, unpythonic, but a single expression
  reduce(sense, measurements)

"You're the first one to pass a programming quiz! --- Dave"

Hah!

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.

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 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.

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.

1. The videos are supplemented by written course notes. This is useful for people (like me) who prefer text to video.

2. There are quizzes to confirm understanding, and they come very shortly after learning a concept. This provides fast feedback.

Crucially, the quizzes can be repeated until you gets the answer right, and an explanation follows.

The first lesson is pretty basic, but the overall learning framework seems promising. It's focussed on making sure you understand.

I'm just using this as a supplement, but it will be interesting to see where it goes.

However I'm also curious what the later units will look like, as I completed the final quiz in a few minutes for the first search engine unit without watching any lectures and without any python knowledge. Very basic stuff.

Thinking of the best way to teach this stuff is something I spend a bit of time doing, and honestly I'm not sure what the right approach is. Once I thought you should learn from Ada's work and the looms all the way up to things like the Enigma machine, and jump into the things that started happening in the 50s research. Basically, grow your knowledge from the ground up and make all the same discoveries.

The other approach is to take a high-level fly by of concepts like strings. Most of us here on HN know that strings and how they work aren't as simple as presented in the 101 videos and there's so much more to know about them.

I'm concerned that this style of teaching (implementing concepts without understanding how they work) is also the style that's lead to a lot of confusion in my personal learning experience as I progressed. Once upon a time everything in the browser like HTTP and rendering was magic and I wrote sloppy web apps. In a similar vein, I never really "got" calculus until I read a book that explained the history of how Newton made up "infinitesimals", Leibniz made up "derivatives" and someone else made up limits. I had no idea how calculus worked until I understood the origins... sure I could write differential equations based on some cheat-sheet, but that deep-rooted understanding never came along until I walked through Newton's shoes. And to be honest, it was simply the dy/dx notation that made no sense to me, and none of my teachers could explain it to me, it was always "oh, that's the derivative of that axis value" (might as well have said "oh, a monad is a monoid in the category of endofunctors").

On the other hand, learning and doing things without that deep understanding is exactly how I started programming and I turned out okay.

When will we see courses on psychology, Spanish, martial arts, etc? What would those courses even look like in an e-learning format?

Judging from our office discussions, the most popular courses would have to be on picking up chicks, making moonshine, and all sorts of crazy money-making schemes.

I know Khan is highly praised, and I have found help there, but I find the site very unstructured. Perhaps it works best when you have an existing syllabus or path to follow next to it.

It's also somewhat limited to college math

One thing that is really throwing me off atm, is all the static functions, sum(list), len(list) as opposed to more OO solutions

They added an improved FAQ to the main page of each course, in the overview section.