I took CS229 here at Stanford and I was also one of the TAs for the online version last year (I was one of 2.5 people involved with making the programming assignments).
First, the Stanford CS229 version is definitely much more difficult than what you guys had online. The focus in the actual class was on the math, derivations and proofs. The homeworks sometimes got quite tricky and took a group of us PhD students usually about 2 days to complete. There was some programming in the class but it was not auto-graded so usually we produced plots, printed them out, attached the code and had it all graded by TAs for correctness. The code we wrote was largely written without starter code and I do believe you learn more this way.
An online version of the class comes with several challenges. First, you have to largely resort to quizzes to test students (instead of marking proofs, derivations, math). There is also no trivial way to autograde resulting plots, so everything has to be more controlled, standardized and therefore include more skeleton code. But even having said all that, Andrew was tightly involved with the entire course design and he had a specific level of difficulty in mind. He wanted us to babysit the students a little and he explicitly approved every assignment before we pushed it out. In short, the intent was to reach as many people as possible (after all, scaling up education is the goal here) while giving a good flavor of applied Machine Learning.
I guess what I mean is that you have more experience than the target audience that the class was intended for and I hope they can put up more advanced classes once some basics are covered (Daphne Koller's PGM class is a step in this direction). But there are still challenges with the online classes model. Do you have ideas on how one can go beyond quizzes, or how one can scale down on the skeleton code while retaining (or indeed, increasing) the scale at which the course is taught?
First, the Stanford CS229 version is definitely much more difficult than what you guys had online. The focus in the actual class was on the math, derivations and proofs. The homeworks sometimes got quite tricky and took a group of us PhD students usually about 2 days to complete. There was some programming in the class but it was not auto-graded so usually we produced plots, printed them out, attached the code and had it all graded by TAs for correctness. The code we wrote was largely written without starter code and I do believe you learn more this way.
An online version of the class comes with several challenges. First, you have to largely resort to quizzes to test students (instead of marking proofs, derivations, math). There is also no trivial way to autograde resulting plots, so everything has to be more controlled, standardized and therefore include more skeleton code. But even having said all that, Andrew was tightly involved with the entire course design and he had a specific level of difficulty in mind. He wanted us to babysit the students a little and he explicitly approved every assignment before we pushed it out. In short, the intent was to reach as many people as possible (after all, scaling up education is the goal here) while giving a good flavor of applied Machine Learning.
I guess what I mean is that you have more experience than the target audience that the class was intended for and I hope they can put up more advanced classes once some basics are covered (Daphne Koller's PGM class is a step in this direction). But there are still challenges with the online classes model. Do you have ideas on how one can go beyond quizzes, or how one can scale down on the skeleton code while retaining (or indeed, increasing) the scale at which the course is taught?