Back to school
Apr. 6th, 2012 05:34 pm![[personal profile]](https://www.dreamwidth.org/img/silk/identity/user.png){kind=small}
lederhosenAt the end of last year I did the Stanford online course on machine learning; this year I'm doing algorithm design and game theory. (I was signed up for one on information theory as well, but it got cancelled, probably good for my workload.)
Aside: My boss^4 has a story that he tells to illustrate his view on theoretical seminar presentations. Old monk, young monk; old monk gives young monk a sieve and tells him to fetch water with it. Young monk spends all day trying to pick up water in the sieve without success, and returns saying "Why did you give me this job? I could never hope to bring back water this way."
"Ah, but look how clean your sieve is!"
Frank's view is that some of these seminars are there to clean our sieves: nobody is expected to understand the content, but they're meant to sharpen our game by showing us what theoretical rigour looks like and remind us that we could be doing better.
I was a little comforted to hear this - I get hit by self-doubt when I can't keep up with a lecturer - but I'm not sold on the approach. To me, the point of a lecture is to let other people understand what it is that you're talking about.
So one of the things I've been really enjoying about these courses is that they're pitched at about the right level. I could probably cope with something 20-30% more demanding, but as it is, I can multi-task while skimming the lectures and I can get warm fuzzies by helping out those of the students who aren't having as easy a time of it.
The ML material is likely to be very relevant for my work; the other stuff not so much, but still Relevant To My Interests.
So, yeah, if you're interested in geeky subjects, the free online stuff at Stanford is worth a look.
Aside: My boss^4 has a story that he tells to illustrate his view on theoretical seminar presentations. Old monk, young monk; old monk gives young monk a sieve and tells him to fetch water with it. Young monk spends all day trying to pick up water in the sieve without success, and returns saying "Why did you give me this job? I could never hope to bring back water this way."
"Ah, but look how clean your sieve is!"
Frank's view is that some of these seminars are there to clean our sieves: nobody is expected to understand the content, but they're meant to sharpen our game by showing us what theoretical rigour looks like and remind us that we could be doing better.
I was a little comforted to hear this - I get hit by self-doubt when I can't keep up with a lecturer - but I'm not sold on the approach. To me, the point of a lecture is to let other people understand what it is that you're talking about.
So one of the things I've been really enjoying about these courses is that they're pitched at about the right level. I could probably cope with something 20-30% more demanding, but as it is, I can multi-task while skimming the lectures and I can get warm fuzzies by helping out those of the students who aren't having as easy a time of it.
The ML material is likely to be very relevant for my work; the other stuff not so much, but still Relevant To My Interests.
So, yeah, if you're interested in geeky subjects, the free online stuff at Stanford is worth a look.
