Lecture live, record the lectures. EVERY speaker is more effective when they have the feedback of their audience's faces.
Expect the members of the public to meet the class' standard, don't shrivel the curriculum to match the public. An applied CS course that doesn't demand programming is bizarre. Flunk everyone if you have to. Grades should perhaps reflect understanding?
I do think that he occasionally would belabor the obvious, but for the intended purpose of the class, I think that it is better that he erred on the side of being too clear, rather than on the side of being opaque.
Vocabulary is very important. But in addition to knowing the meanings of words, its important to know why concepts are carved up the way they are. Why divide the world into supervised and unsupervised --- what does carving at that joint win us?
Getting people interested is what the first week is for. The other eight, I feel, should try to satisfy the curiosity the first week sparked.
I'm not sure that I get you. Why we have both supervised and unsupervised algorithms was made pretty clear to me in the class. And just what more difficult material would you have liked to seen? The real Stanford class is made more difficult largely by doing lots of difficult math proofs. I emphatically disagree with any assertion that this free class should be so math heavy. What would be the purpose of that for the intended audience? Sure, such work would make fine extra credit, but making effective use of algorithms rarely relies on on a ability to mathematically prove that they have the properties that they do.
I think that a big final project, as is required in the real Stanford class, on the other hand, would have a lot of utility for the intended audience. But as that would be impossible to automatically grade, that's a non-starter.
My own experience with ML is so narrow, it's hard for me to say what else should have made an appearance --- I made up the example that you rightly called me on. I worked with a research group on a reinforcement-learning system with a silly name, and we used a whole messy pile of linear algebra. I guess I shouldn't have expected to recieve the same kind of grounding in all the ML topics.
I agree that projects would make for a huge improvement. They are difficult, but no more difficult than they need to be, and practical. You're right, grading 20,000 of them would be madness. And maybe that's OK --- what's wrong with assigning work that won't be graded?
(OK, there are lots of things wring with it, starting with motivating the student to do the work, and ending with the lack of quality feedback. But even suggesting projects for self-directed students might be enough to get a blog-ecosystem going.
I don't know much about pedagogy --- I'll talk to some people who do, and maybe they'll dope-slap me into agreeing with you totally.)
But I don't think we disagree particularly -- math should only appear when necessary to understanding. Easy things shouldn't be made difficult just for the sake of some kind of scholastic masochism. But difficult things should be attacked with vigor, and not nerfed for the sake of the audience.
I've heard some people complain that the programming assignments in the Machine Learning class were too easy. A specific complaint is that they provided all the equations and explication you needed right in the homework statement rather than having to remember it from the lectures. Personally, I find this approach to be the best way to learn. My favorite approach to learning has always been "workbook" based, where the lessons and problems to solve are in self-contained lessons. Give me material like that and I can learn anything. There are entire classes at MIT that I did extremely well in because they were workbook based. And I took Organic Chemistry and got an A+ in the first half of the class because it was workbook based. They thought I was a genius. Then the second half of the class used the more traditional approach of reading 100 pages a week of terribly boring and dense textbook. I got a D- in that half. Fortunately, it averaged to a C and I passed the class, but if the entire class had been workbook based, maybe I'd be doing something great with Computational Chemistry at the moment.
Back to the actual ML class, I've only completed the first few programming exercises as of yet, as I was also taking the database class, which actually turned out to be a lot of work. I've heard that in the ML class, the programming exercises become progressively spoon-fed, and ultimately not much of a challenge. That's not good, if true. While I do think that all the information you need should be at hand, you should still be given challenges that make you think. All the thinking should not be done for you.
But when the lecturers at hand are professors who have spent decades teaching to a room full of students who become visibly impatient, confused, eager, or antsy --- they should keep doing that.
To keep a pleasant rhythm to instruction, many professors tell jokes; I think it's VERY hard to tell a joke to a webcam when you are used to an audience.
My worry, which is similar to the worry of the student in the linked post, is that these lectures may be devaluing the in-person versions of the course. CS 229 was one of the top 5 hardest classes I have ever taken. I would rather not someone else take it via a dumbed down online lecture and say "I did that too! It wasn't so bad!"
There was one "brutally difficult" class at MIT that I loved, though: Structure and Interpretation of Computer Programs. It would be nice if all brutally difficult classes came in a less brutal version, though. That level of difficulty is great if you adore the subject matter, as I adored SICP, but if you only like it instead, this type of class can give you a permanent aversion for the material rather than transform you liking into love.
I feel that MIT's Signals and Systems did this to me. There's no way that having to do ten pages of algebra (e.g., Laplace transform, inverse Laplace transform, simultaneous equations) to solve a single homework assignment, multiplied by ten for the entire problem set, was ever going to do anything but give me a permanent aversion for the subject matter.
But at the same time, some of the classes had very difficult subject matter and the professors were actually interested in teaching students instead of torturing them. So it wasn't totally a bad experience.
I guess there is some value in having these online classes because they motivate professors to make their subject matter more palatable. 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.
When I worked at Harvard a few years ago, there was a tempest in a tea pot over Harvard Extension and Hillary Duff. The media got wind that Hillary was taking some distance learning classes via Harvard Extension and referred to her as a "Harvard student". Harvard Extension is an excellent institution, and Harvard should be commended for running it, but it is rather different from Harvard College and Harvard Graduate School of Arts and Sciences. Harvard Extension, for instance, has no admissions requirements. They accept everyone, but you can't stay in it unless you get decent grades.
In response to the news stories about Hillary, The Crimson published an editorial that was extremely snide about Ms. Duff being a "Harvard (extension) student". This editorial did not make the undergraduate population of Harvard look good. It made them look like a bunch of unsympathetic brats. Sure, it might be a bit annoying if you feel that any ol' person can claim to be a Harvard student when you worked hard your entire life to get into Harvard. On the other hand, when it matters, there is unlikely to be any real confusion, and there is no real devaluation of a Harvard College degree.
It is true that some people do through a bit of vagary to pass off their Harvard Extension degree as a Harvard College or Graduate School degree, but when you catch someone in this, it just makes them look rather bad.
In summary, I hope that Stanford students don't make the same mistake that the Harvard Crimson made. If I didn't know a bunch of Harvard students personally, the Crimson article would have reinforced a bad taste in my mind about Harvard students in general.
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 as rigorous as the "real" Stanford class.
There's some actual danger in devaluing Stanford's rep here.
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.