101–107 of 107 posts
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Were you a quant before you took the UW classes?
Nope. My background is in engineering so the calculus wasn't an issue, but my stats knowledge was really weak.
Perhaps this class happened to be taught at an easier-than-usual level. But, if professors are torn between the demands of simultaneously serving dedicated (paying or non-paying) students and casual students, the compromises won't always be to the benefit of the dedicated.
I think the issue here is what we are witnessing, aside from the awesomeness of open courseware, is the evolution and continued maturity of Computer Science as a discipline, as it takes more and more mathematical concepts into its fold.
Machine Learning is growing up as a foundational pattern / algorithm that has evolved from research to applied to basic-building-blocks-everyone-should-know. Yes, it took the Valley and Stanford to liberate it (as a poster indicates somewhere here), but that's ok. There is as much street cred in understanding how to implement/design practical applications using ML as there is in pushing the frontier on new ML mathematical techniques. That's how new commercial innovation takes place. You need both sides of the equation; the research and the practical. The course chose a balance that favored the latter, because prior to this little existed. You can hear it in Prof. Andrew Ng's videos... "this is big in the valley"..."you now know how to implement XYZ"... "if you ask these questions on an ML product, you can save your tea, time and money" (I'm paraphrasing).
Recall that at one point, logic/truth tables, sorting algorithms, graphs, etc. all needed to be derived mathematically with their proofs. But then they became axioms and codified as foundational building blocks of CS that just work, enabling us to focus on the next step in the evolution. We don't question or even think twice today when implementing "if (X && Y)".
Earlier quoted context omitted.
I'm sorry if I made it confusing, but it really isn't about whether I signed up for 229 or 229a, the point is most of my classes for the coming term are now under this new online format and it would be shame to see them lower their standards to fit the public.
It's a good point -- I guess we'll find out next semester if the difficulty being below average had to do with the nature of the class itself (the a in 229a) or the audience it was being developed for. I hope, if the classes you mention are listed under the same course number they always have, that the difficulty won't be less than usual. For the rest of us, what classes are these? Part of the reason an easier class…
this i guess is not to ease for lazy slobs (what you described :) but to ease the demand for help on the staff.
The critic is that they overdid
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I just don't want my degree devalued by people who aren't as astute as yourself when it comes to the gap between the online courses and those taken by Stanford students. I can certainly understand this worry, but I don't think you really have to worry too much about this. Those who matter will always know the true value of a Stanford education. It's not quite as good as an MIT education ;) but it's right up there. Wh…
The classes were presented as being the same as Stanford classes, and in the AI class one of the professors claimed to be surprised that the online students were doing better than the Stanford students. As if the comparison was valid based on the difficulty of the material. You can't make those sorts of claims and at the same time try to protect the reputation of the institution by saying that the online class is not…
In the database class they did say that this class wasn't terribly different from the real Stanford class, but that the Stanford students would probably be assigned a bit more of the problems that were particularly challenging. Those questions, however, were pretty much the least valuable questions in the entire class, since they had little relationship to any kind of query you'd ever use in the real world. I.e., they're the kind of question that teachers like to put in there so that you can have a more distinct grading curve. In any case, the database class was a fair amount of work, so I don't think that it is likely to make a Stanford education seem too easy.
The Machine Learning class, on the other hand, never made any claim at all of being remotely like the real Stanford class. Their only claim was that it would give you a good foundation to use Machine Learning techniques in the real world. That is a completely true statement. As it turns out, however, Stanford did offer the free Machine Learning class that I took as a real Stanford class for those at Stanford who wanted an easier version of Machine Learning.
There are two goals with learning ML: - Applying known ML algorithms to a real world task - Coming up with new ML algorithms and research It turns out (ahem) that the first goal, applying ML, is not only much, much higher in demand than the second goal, but is also much, much easier to teach than the content required to pursue the second. This dichotomy is the reason the split between CS229a and CS229 in both content…
I'm sorry if I made it confusing, but it really isn't about whether I signed up for 229 or 229a, the point is most of my classes for the coming term are now under this new online format and it would be shame to see them lower their standards to fit the public.