Hyphen Class Post-Mortem

Last fall I signed up for two of the hyphen classes: the Machine Learning ml-class (Ng) and the Artificial Intelligence ai-class (Thrun and Norvig). Both were presented by Stanford professors but one of the conditions of taking the courses was that whenever I discuss them I am required to present the disclaimer that THEY WERE NOT ACTUALLY STANFORD COURSES and that I WAS NEVER ACTUALLY A STANFORD STUDENT and that furthermore I AM NOT FIT TO LICK THE BOOTS OF A STANFORD STUDENT and so on. (Caltech is better than Stanford anyway, even if whenever you tell people you’re in the economics department they always say, “we have one of those?!”)

My background is in math and economics, but I’ve taught myself quite a bit of computer science over the years, and I consider myself a decent programmer now, to the point where I could probably pass a “code on the chalkboard” job interview if that’s what I needed to do in order to support my family and/or drug habit.

I’d worked on some machine learning projects at previous jobs, so I’d picked up some of the basics, but I’d never taken any sort of course in machine learning. At my current job I’m the de facto subject matter expert, so I thought the courses might be a good idea.

The classes ended up being vastly different from one another. Here’s kind of a summary of each:

ml-class:

* Every week 5-10 recorded lectures, total 1-2 hours of lecture time. (There was an option to watch the lectures at 1.2x or even 1.5x speed, which I always used, so it might have been more like 3 hours in real-time. This means that if I ever meet Ng in real-life, he will appear to me to be speaking very, very slowly.)

* Most lectures had one or two (ungraded) integrated multiple choice quizzes with the sole purpose of “did you understand the material I just presented?”

* Each week had a set of “review questions” that were graded and were designed to make sure you understood the lectures as a whole. You could retake the review if you missed any (or if you didn’t) and they were programmed to slightly vary each time (so that a “which of the following are true” might be replaced with a “which of the following are false” with slightly different choices but covering the same material).

* Each week also had a programming assignment in Octave, for which they provided the bulk of the code, and you just had to code in some functions or algorithms. I probably spent 2-3 hours a week on these, a fair amount of that chasing down syntax-error bugs in my code and/or yelling at Octave for crashing all the time.

* Machine learning is a pretty broad topic, and this course mostly focused on what I’d call “machine learning using gradient descent.” There was some amount of calculus involved (although you could probably get by without it) and a *lot* of linear algebra. If you weren’t comfortable with linear algebra, the class would have been very hard, and the programming assignments probably would have taken a lot longer than they took me.

* The material was a nice mix of theoretical and practical. I’ve already used some of what I learned in my work, and if there was a continuation of the class I would definitely take it. As it stands I’m right now signed up for the nlp-class and the pgm-class, which should be starting soon, both of which are relevant to what I do.

* The workload, and the corresponding amount I learned, were substantially less than they would have been in an actual 10-week on-campus university course. This was great for me, since I also have a day job and a baby. If I were a full-time student being offered ml-class instead of a real machine learning class, I might feel a little cheated. (I saw a blog post by some Stanford student whining about this, but he was mostly upset that the hyphen classes were devaluing his degree. Someone should have reminded him about the disclaimer.)

* The class was very solidly prepared. The lectures were smooth and well thought out. The review questions did a good job of making sure you’d learned the right things from the lectures. The programming assignments were good in their focus on the algorithms, although that did insulate you from the real-world messiness of getting programs set up correctly.

* It certainly seemed like Ng really enjoyed teaching, and at the end of the last lecture he thanked everyone in a very heartfelt way for taking the class.

ai-class:

* Every week dozens of lectures, each a couple of minutes long, interspersed with little multiple choice quizzes. This was my first point of frustration, in that the quizzes were frequently about parts of the lecture that hadn’t happened yet. Furthermore, they often asked ambiguous questions, or questions that were unanswerable based on the material presented so far.

* Each week had a final quiz that you submitted answers for one time only. Then you waited until the deadline passed to find out if your answers were correct (and then you waited another day, because the site always went down on quiz submission day, and so they always extended the deadline by 24 hours). These quizzes were also ambiguous, which meant that if you wanted to get them correct you had to pester for clarifications (and sometimes for clarifications of the clarifications).

* This resulted in the feeling that the grading in the class was stochastic, and that your final score was more reflective of “can I guess what the quiz-writer really meant” than “did I really understand the material”. Although I didn’t particularly care about my grade in the class, I was still frustrated and disheartened by the feeling that the quizzes were more interested in *tricking* me than in helping me learn.

* What’s more, the quizzes often seemed to focus on what seemed to me tangential or inconsequential parts of the lesson, like making sure that I really, deeply understood step 3 of a 5-step process, but not whether I understood the other four steps or the process itself.

* The material also seemed very grab-bag, almost like an “artifical intelligence for non-majors” survey course.

* Anyway, partly on account of my finding the class frustrating, partly on account of time pressures, and partly because I didn’t feel like I was learning a whole lot, I dropped the ai-class after about four weeks.

* There were no programming assignments, but there was a midterm and a final exam, both after I quit the course. From what I could tell, they were longer versions of the quizzes, with the same problems of clarity and ambiguity. (I never unfollowed the @aiclass twitter, and during exam time it was a steady stream of clarifications and allowed assumptions.)

* Compared to the tightly-planned ml-class, the ai-class felt very haphazard. In addition, the ml-class platform I found more pleasant to use than the ai-class platform.

* I quit long before the last lecture, so I have no idea how heartfelt it was.


One thing about both classes: I *hate* lectures. I learn much better reading than I do being lectured at, and I found the lecture aspect of *both* classes frustrating. I have complained about this in many venues, but my prejudice is that if you’re using the internet to make me watch *lectures*, you’re not really reinventing education, because I still have to watch lectures, and I hate lectures. Did I mention that I hate lectures?

By way of comparison, I have also been doing CodeYear. It is currently below my level (I am plenty familiar with variables and if-then statements and for loops), but I don’t know much Javascript, and the current pace makes me hopeful that it will get interesting for me after another month or two.

If you don’t know that platform, it gives you a task (“create a variable called myName, assign your name to it, and print it to the console”) and a little code window to do it in. Then you click “run” and it runs and tells you if you got it right or not. There is a pre-canned hint for each problem.

What I really like about Codeacademy is that I can do it at my own pace. The lessons are wildly variable in quality, but I’m glad not to have to sit through hours of lectures every week. They also do “badges”, which I find more satisfying than I wish I did. That said, I suspect someone with no experience debugging code would find the experience impenetrable and waste hours tracking down simple syntax errors, and indeed I saw on Hacker News a post to this effect a few weeks ago.

In the end, despite all this, the way I learn best is through a combination of reading books and writing actual code. I’ve had to learn F# over the last month, which I’ve done by reading a couple of (quite nice) books and writing a lot of actual code. It’s hard for me to imagine the course that would have done me any better (or any faster).

Similarly, if I wanted to learn Rails (which some days I think I do and other days I think I don’t), I have trouble imagining a course that would do better for me than just working through the Rails Tutorial (which I have skimmed, which has convinced me that I could learn well from it).

Similarly similarly, I suspect that the right Machine Learning book (and some quality time with e.g. Kaggle) would have been much more effective for me than the ml-class was. But if such a book exists, I haven’t found it yet.

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