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Comment by gfodor | Hacker News Reader

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 and audience works so well. The demand for CS229 is low and the rigor is high (so it should be priced high), and the demand for CS229a is high and the rigor is low (so it should be priced low.) The author signed up for the wrong class. I think Stanford is teasing out subfields of CS that have this quality, and there sure are many.

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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 is better for the public is because there's just less time for working people. If the difficulty/time commitment for the offered classes next semester are substantially more than 229a I'd imagine you'll see a much higher dropout rate not due to lack of ability but due to lack of time. It seems odd Stanford would try to pack a "real" course into the online/public format, but at the same time seems odd they'd dilute a "real" course for their undergraduates.

The easier assignments are easier because they now have morehints alongside.

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

I agree, but would prefer to keep the standards high for the public rather than see the courses separated. I enjoyed the first few ml-class lessons, but I quickly became bored with the difficulty level.

But should classes be hard for being hard's sake, or should they be hard when learning the material requires it?

The secret is: This Is The Internet.

There's no reason a class can't have the same material presented two (or three or twelve) different ways for multiple audiences.

There's no reason a class needs a "start date" and an "end date." We can grow classes to be about communities, not authorities. None of us are as smart as all of us.

We shouldn't copy the Boring Ole' Lecture Format (and especially not "camera up my nose while I write on paper") when we could be doing so much more.

You're right, and I hope we'll see more innovation in this area, but I don't think it's reasonable (or a good idea) to expect Stanford professors to try to attack two very hard problems at once at this stage:

- Scale traditional educational techniques (lectures, homeworks, schedule) to a class size of 100,000 people

- Come up with an altogether new way to teach and assess students by taking advantage of the web

It's probably a good idea to figure out the bugs in solving the first problem before tackling the second.

Except Nikola Tesla. All of us are still not as smart as Nikola Tesla

I disagree. The start/end date concept facilitates working together with others in a study group setting. Everyone is at the same part of the class, and you can benefit from learning together. Finding a real-world study group for material with no start/end date is significantly more difficult. One of the best ways to learn is to work through material with others in a group.

If the lectures were coupled with wiki-style video annotation, over time people could curate Q&A. This would be fantastic study material and it would be embedded at the most relevant moments of the lecture. It would get better with each new question and would be available to everyone forever.

So - people still learn together, just not necessarily concurrently.

I disagree. This class was very easy compared to other courses in the department. There's no problem with the class not having a theoretical focus. However I do think that it is bad to have assignments/quizzes that require no critical thinking and programming assignments that can be done in 30-40 minutes. There is a lot that can be taught in the area of applied machine learning.. I have to disagree that applied machine learning is inherently an "easy" topic.

I think this course would be great for what it does if they made the programming projects a bit harder and maybe labeled it an intro level course (CS129?). I totally agree with the posters sentiments -- I'd much rather have a harder class where you interact with other students to learn more complex material.

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