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I read some of the ML lectures notes from OCW some time back. It was very math heavy. Is that an MIT thing or is that how ML is taught in all universities?

by FraaJad 18y ago

aditya 18y ago
ML is math heavy. And, in any academic setting that's how it's going to be taught, I'd be interested in practical ML references as well...
jerf 18y ago
I can not conceive of how one would have a "non-math heavy" machine learning course. Machine learning is basically a branch of applied statistics, at least in its current form. None of the algorithms are "fire and forget", such that you can use them effectively without deeply understanding them, and one of the first things you'll cover in your Machine Learning course is why it is unlikely that will even happen (the No Free Lunch theorem, often over-applied in internet debates but still a very, very important result). (I mean, sure, you can try to fire-and-forget, but for the most part you'll either fail with no clue why, or fail to get very good results. Decision trees might be a moderate exception, although even then you really ought to understand how they are constructed and the implications of various choices you can make.)
- kurtosis 18y ago
  As a counterpoint, I seems to me that the ML field is too heavy on theory. A lot of the theoretical papers are proposed with weak or no experimental tests. There are very few people doing thorough experimental work. What I have in mind is something like Chen and Goodman's comparison of different language models - this is a great paper and I'd love to see more like it. The obvious example of this is boosting where the theory lagged far behind the experiment - everyone wanted to invent a theory to explain why boosting was so effective. hey industrialists out there - publish!
  - physcab 18y ago
    We're trying! I'm using machine learning to detect explosives. It's a difficult problem, but it works quite well.

I read some of the ML lectures notes from OCW some time back. It was very math heavy. Is that an MIT thing or is that how ML is taught in all universities?

by FraaJad 18y ago

I read some of the ML lectures notes from OCW some time back. It was very math heavy. Is that an MIT thing or is that how ML is taught in all universities?

aditya 18y ago

ML is math heavy. And, in any academic setting that's how it's going to be taught, I'd be interested in practical ML references as well...

jerf 18y ago

I can not conceive of how one would have a "non-math heavy" machine learning course. Machine learning is basically a branch of applied statistics, at least in its current form. None of the algorithms are "fire and forget", such that you can use them effectively without deeply understanding them, and one of the first things you'll cover in your Machine Learning course is why it is unlikely that will even happen (the No Free Lunch theorem, often over-applied in internet debates but still a very, very important result). (I mean, sure, you can try to fire-and-forget, but for the most part you'll either fail with no clue why, or fail to get very good results. Decision trees might be a moderate exception, although even then you really ought to understand how they are constructed and the implications of various choices you can make.)

kurtosis 18y ago
As a counterpoint, I seems to me that the ML field is too heavy on theory. A lot of the theoretical papers are proposed with weak or no experimental tests. There are very few people doing thorough experimental work. What I have in mind is something like Chen and Goodman's comparison of different language models - this is a great paper and I'd love to see more like it. The obvious example of this is boosting where the theory lagged far behind the experiment - everyone wanted to invent a theory to explain why boosting was so effective. hey industrialists out there - publish!
- physcab 18y ago
  We're trying! I'm using machine learning to detect explosives. It's a difficult problem, but it works quite well.