It is surprising that you thought of this as a "cool AI job offer". I have two remarks here. First, the email sent to the class is barely an invitation to send resumes. Something many programmers/CS Students with online presence experience on a regular basis. Probably not from a Stanford Professor but at least from major companies recruiters. It would be interesting to know how many will actually make it through the screening, phone/on-site interviews and get a job offer.
Second, I registered for the Machine Learning course (I am not sure if the same applies to the AI course) and I compared it with the actual ML course at Stanford (CS229) (I mainly looked at Youtube videos of Andrew as well as Assignments/Midterm). The latter is by far more advanced and theoretical. The assignments tend to test more than basic comprehension of the material presented in the lectures, which is exactly what the online course reviews tend to evaluate. They require strong mathematical knowledge and obviously a minimum level of creativity/intelligence.
"It is surprising that you thought of this as a "cool AI job offer"."
I don't. That part of the post was written with tongue firmly attached to cheek. If that tone didn't come through, that means I have to improve my writing.
The online ML course is CS 229A (which is also an actual course at Stanford. The online version is close to the Stanford course).
The "tough" version is CS 229 (no 'A' at the end). I registered for the ML course thinking it was an online version of CS 229 and dropped out when it was confirmed to be 229A. In my politically incorrect opinion, 229A is close to worthless. The math is important in real world ML. This course included gems such as "if you don't know what a derivative is, that is fine".
The online AI course is almost exactly the same course as Stanford (CS 221), minus, of course, the programming assignments. It is an introductory, broad based course, and it does the job well (imo)
The online DB course is almost (if not exactly) the same as Stanford CS 145. I think this was the best course of the three.
All courses track the corresponding Stanford courses.
> 229A is close to worthless
> This course included gems such as "if you don't know what a derivative is, that is fine".
It also included other gems like debugging models with learning curves, stochastic gradient descent, artificial data and ceiling analysis. I have not come across practical things like these in more mathematically oriented ML books that I have tried reading in the past.
Interestingly, your arrogance is in sharp contrast with the humility of the professor, where he admits in places that he went around using tools for a long time(like SVM) without fully understanding the mathematical details.
> "if you don't know what a derivative is, that is fine".
A bit of me died when I heard prof. Ng say that. However, I had committed to finishing ml-class and I did. As of now, I'm glad I went through with it. I felt like I was learning all these cool AI techniques that I hadn't heard about. However, the proof is in the pudding. The question is will I be able to take a real world problem and apply what I learned in that class to come up with something interesting? If I can't you are probably right. My perfect record would only be worth the paper it's printed on and the money I paid for the course!
I'm not pointing fingers at Prof. Ng. or anyone here. It was an experiment for Stanford and an experiment for me. I know I am looking forward to the courses next year :).
On the other hand, it you already know what a derivative is, you already went through all the lineal algebra stuff, have an idea of numerical methods, etc, I appreciate not wading into those side areas. Specially if you have kids, a dayjob and doing the AI-class at the same time :D
CHI is a huge conference. More than 1000 papers were submitted last year. A designated committee chooses 1% of the submitted papers to receive the best papers awards which explains the number of awarded papers.
Having 2 or 3 best paper awards I can understand, but having a dozen is cheating. Many of the other top conferences like WWW, AAAI, and SIGIR have nearly 1,000 submissions also and they only have a single best paper award.
Personally, I much prefer searching citeseer, as most of the papers I've found through google scholar are behind paywalls, while virtually every paper on citeseer is free.
If I can't find a paper through citesser, I usually just do a regular google search, and often find the paper elsewhere on the web. Google scholar is pretty much my last resort, and I really haven't had much luck finding freely downloadable papers through it.