Anders Brownworth

Technology and Disruption

Online Education: Cal Tech vs. Stanford Machine Learning Courses

I went through two Machine Learning courses on iTunesU, the Caltech Machine Learning course taught by Yaser Abu-Mostafa and the Stanford Machine Learning course by Andrew Ng. They almost couldn't have been more dissimilar in approach. Initially I started with Andrew Ng at Stanford. Immediately he jumped into the math surrounding machine learning and didn't divert much from the theory for the first few classes. I suppose some people might learn well by starting from the theory, but not me. I switched over to Yaser Abu-Mostafa's Caltech course. While still thankfully math heavy, he started firmly grounded with the reasons for why one approach might be a better choice than another using examples.

I think this represents a fundamental strength of online learning. I'm a huge fan of Stanford; I find most of their computer science classes on iTunesU fantastic. In fact, I learned iPhone development back in the early days by watching Stanford's CS193P, but the machine learning class wasn't ideal. Rather than be stuck with that, I could cherry-pick a better class for my learning style over at Cal Tech.

This reminds me of a Freekonomics story on online learning by Sanjoy Mahajan. There was a Stanford class done live with students at Stanford and an online audience. The total enrollment was around 16,000 students and of them about 200 were matriculating students at Stanford. 248 students got perfect scores in the course and none of them were Stanford students. Did this point to some fundamental lapse in the admissions process where these students were being overlooked? Were they not applying? Or did it have something to do with the other things happening around each of those students when they were taking the class? I imagine it is a bit of all of these reasons but most significantly, I think it has to do do with pace and fit which are things you can manage as an online student where you would have a much more difficult time physically attending the class. (that and the sample size of only 200 Stanford students)

I can stop the lecture whenever I want and go research a particular topic or even snag a 10 minute Khan Academy refresher on how to do matrix math whenever I need. I can also cherry pick the best course for my learning style even across institutional bounds if things aren't exactly the right fit. About the only benefit I won't get doing all of these classes over the years is institutional credit. I don't really care about that at this point, college credit has long since lost its impact on my ability to be hired. Credit doesn't motivate me to do these courses, interest in the content does.

I believe credit, which is more of a problem for younger learners, will be effectively solved in the next 5 to 10 years. Some system employers recognize will emerge, with or without institutional buy-in. What is not clear to me is what model will be the winner at this point. Will the colleges and universities get together and make a cross-institutional system that assigns something similar to a certificate of achievement or even a traditional degree, or does some third party accreditation system emerge which evaluates courses become respected? I don't know at this point but I am watching Stanford in particular. Watch Stanford president John Hennessy discuss this in an interview from All Things D back in June if you want some idea where this is headed.

Comments (2)

Anders from Cambridge, MA

If you think about it, the long-term ramification of this is that in a majority online world where the classroom is flipped and class time is used more for mentoring and exercise problems, the teachers that are great at mentoring will excel. Those teachers that don't mentor well but traditionally have taught well will either be generating the teaching content or will be essentially irrelevant in the new model.

ilana from israel

Is there any other way you can recomend to study it.
a moew practical explanations just describing the alogrithms step by step , and not focusing on the theoretical proof

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