Thursday 10 November 2011

Dropping Stanford’s Online AI Class

I have decided to stop following Stanford’s online AI course to focus on the sibling Machine Learning and Database classes. Simply put, I feel that I’m learning a great deal more from the ML and DB classes, and the AI class isn’t really giving me a sufficient return on investment for my time. I’d like to jot down a few thoughts as to why this is the case.

Now, it may seem churlish to criticise a course being put out for free, so let me first clarify that I do heartily appreciate the generous efforts of Sebastian Thrun and Peter Norvig in running this; it must take an inordinate amount of time and effort to do so. I’m also convinced that online courses like this are a tremendously important future direction for education. Even so, I believe that it’s appropriate to evaluate and give constructive criticism -- much like reviewing a piece of open source software built by volunteers, for example.

I think the biggest weakness of the AI course is the lack of any practical assignments. Both the ML and DB classes have hands-on assignments for the week’s topics, and it really helps to understand and motivate the material. The AI class lacks any equivalent; indeed, for some of the weeks, only half the topics appeared even on the homework problems. Clearly, if I were sufficiently motivated, I could go and implement these things for myself, but part of the purpose of a course is as external motivation. After a hard day at work, it’s often only the threat of a deadline that will get me to study ;-)

The homeworks that have been set have been, in my opinion, rather low quality, with lots of ambiguity or errors requiring clarification after the fact...or even giving the answer to one question in the next! While I’ve scored well on them, I don’t necessarily feel it corresponds to any deep understanding of the material. It’s quite possible to answer many homework questions just by mimicking the mechanics of a lecture video by rote without any deep comprehension of what’s going on.

The lectures themselves are divided up into lots of short videos with quizzes at the end. Unfortunately, I found the constant quizzes to be simply annoying. I understand that asking questions to provoke the listener to think through a topic is good pedagogic technique, but it can definitely be overused. Rather than prompting me to think through a question, my response often ended up being “how the %£$! should I know?”  Further, because of this, the videos aren’t really set-up for offline viewing. Most of my study time is on the train commuting, so this was a big downside. For the ML and DB classes, the videos are divided into a few chunks with download links, which makes them practical for offline use.

Some of the pacing of material could be better -- the unit on probability that rapidly went from easy examples of a biased coin to some involved Bayesian probability calculations seemed particularly ambitious. There have also been a few technical issues caused by the large number of people using the site -- this one is entirely forgivable, given the size of the class, but the ML and DB class infrastructure has been very stable by comparison.

Again, to reiterate, I strongly support the concept behind these courses and appreciate the hard work that has gone into them. I’m also aware that there are plenty of people who are really enjoying the class. If I had more time available, I probably would stick with it, but it’s not really worth it for me at this point.