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Do you know whether Prof. Ng has updated the material since the first run of the class?

We are still in the honeymoon phase of free, online university courses, so I think there's been relatively little criticism of what's available now, but I'll go for it: I was disappointed by the Coursera/Stanford ML class. It was obviously watered down, the homeworks were very (very) easy, and I retained little or nothing of significance.

In contrast, the Caltech class was clearly not watered down, and, as the material was much more focused (with a strong theme of generalization, an idea almost entirely absent from the Stanford class, as I recall) I feel I learned far more.

Another big difference: the Caltech class had traditional hour-long lectures, a simple web form for submitting answers to the multiple-choice* homeworks, and a plain vBulletin forum. The lectures were live on ustream, but otherwise, no fancy infrastructure.

So I think that some interesting questions will come up. Do we need complex (new) platforms to deliver good classes? For me, the answer right now is no -- what clearly matters is the quality and thoughtfulness of the material and how well it is delivered. Can a topic like machine learning be taught effectively to someone who doesn't have a lot of time, or who doesn't have the appropriate background (in CS, math)? Can/should it be faked? I don't think so, but I think there are certainly nuances here.

* Despite being multiple-choice, the homeworks were not easy -- they typically required a lot of thought, and many required writing a lot of code from scratch.

One of the conscious aims of the undergraduate coursera classes has been to lower the bar (in terms of assumed prerequisites, pace, and scope) in order to increase participation.

Daphne Koller's Probabilistic Graphical Models was their first graduate class and it was definitely tougher than other Coursera offerings have been.

This. The Coursera PGM class is the only free online class that I've enrolled in that felt like a similar difficult to a slightly harder than average undergrad course at Caltech (where I go to school).

Somewhat of a side-topic, but I just finished the Coursera compilers class. It didn't seem watered down to me, covering regular expression (including NFA and DFA representations), parsing theory and various top-down and bottom-up parsing algorithms, semantics checking (including a formal semantics notation), code generation (with formal operational notation), local and global optimization, register allocation and garbage collection.

I guess it was partially watered down in that the programming part of the class was optional.

The Coursera ML class is nowhere near the Stanford-level class in terms of academic rigor.

That being said, several of my peers who didn't go to the school really appreciated it for its accessibility.

I think the expectation of that class is to render ML education accessible and palatable, not to train everyone at an elite level. As this field grows, I'm sure the needs of various parties would be filled to an extent.

I don't think he has. In hindsight, I guess it does seem watered down - but personally, that is ok, I enjoy the pace / difficulty level right now.

However, I'm glad you pointed it out, because I'm eager to learn about ML & hope to use this (CalTech) material to augment the foundation I get from the coursera class.

I think the courses are great to get an idea of what the subject is about. If you face a related problem at least you will know wether it can be efficently solved. It will allow you to speak to an expert in the field at a basic level at least. That said, they certainly can be greatly improved.

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