Some folks in our lab (including myself) have embarked on a little experiment, which is semi-informally taking a machine learning class this summer. We’re taking a machine learning class over the summer in a self-directed manner, including doing all the homeworks. The rules are that the people who don’t do the homework have to pay for the lunches of the people who do do the homework. So far, everyone’s paid for themselves. For now… :)
Anyway, this is the first class I’ve taken in well over 10 years (although I’ve taught a bunch since then), and I’m enjoying it immensely! It also feels very different than when I took classes in the past. Firstly, I’m definitely slower. I’m taking way longer to get through the problems. At least partly, I think this is because my brain is not quite as quick as it used to be, for sure. Not sure if that’s just from having a lot of distractions or lack of sleep or just the aging process, but it’s definitely the case. Lame.
Also, I’m slower because my approach to every question is very different than it used to be. When I was an undergraduate taking a bunch of classes, a lot of the time I was just trying to get the answer. Now, with a lot more experience (and a very different objective function), I’m far less concerned with getting the right answer, and so I of course spend a lot more time trying to understand exactly how I arrived at the answer.
More interestingly, though, is the realization that beyond just trying to understand the answer, I’m also spending a lot more time trying to understand why the professor asked the question in the first place. For instance, I just worked through an example of a decision tree and entropy, and while I think my earlier self would have just applied the formulas to get the answer, now I really understand why the problem was set up the way it was and why it’s trying to teach me something. This is something I think I’ve come to appreciate a lot more now that I’ve taught a few courses and have designed homework and exam questions. When I write a question, I’m usually trying to illustrate a particular concept through an example (though I typically fail). As a student, I think I typically missed out on these messages a lot of the time both because I was more concerned with getting the answer and because I didn’t have the context in which to understand what the concept was in the first place. Now, I’m purposefully trying to understand why the question is there in the first place from the very get go.
(Note: it’s really hard to devise questions that reveal a concept to the student. Lots of reasons, but one of them is that I feel like concepts come across best through interaction. Problems for classes, though, typically have to be well defined with clear statements and solutions. In a way, that’s the worst way to get a concept across. Not sure exactly what the right way to do this is.)
Another thing I’ve noticed is that every mathematical operation I perform, from doing an integral to inverting an equation, seems far more meaningful than it used to. I think it’s because I feel like I have a much deeper understanding of why they come up and what they mean. That makes computations a bit slower but far more purposeful (and with less time spent on fruitless directions).
Which leads to another point, which is that I tend to make fewer mistakes than I used to, especially of the silly variety. I think this is because in our research, a mistake is a mistake, silly or not, and having the right answer is the only one that matters. So I’ll take it slow and get it right more often than before, which is a somewhat amusing change from the past.
Anyway, overall, a really fun experience, and one that I highly recommend if you haven’t taken a class in a while.