Now that I think about it, I do think that I heard somewhere that the free AI class has a final project. IIRC, it's a "challenge" that is the same for every student, and the solutions are ranked by how well they solve the problem according to some metric that can be measured automatically. Kind of like how Netflix set up a challenge like that with a million dollar prize, only in the AI class there was no cash prize. (I hear that they did send out requests for job interviews, though, as rewards.) This actually sounds like a great idea to me. Topcoder for AI. I'm sure something similar could be devised for the Machine Learning class. Now that I think about it, I'd actually be pretty surprised if they don't do something like this for future versions of the class.
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.
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.