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Prof Thrun mentioned in the last office hours that there were about 1650 people with "perfect scores" and he seemed a little thunderstruck by that and said he would admit these folks into Stanford if he could, because they are (paraphrasing, from memory)"Stanford quality".

What struck me was how much importance he gave to this metric, which isn't that hard to game on an online offering.

I know a few people (who shall remain nameless) who collaborate and check each others answers and so on before submission, in direct violation of the Stanford policy (and have 100s or close to it), and so probably have received this mail, whereas more "deserving" (note quotes) people who honestly work through the course material may not because they have, say, a 85% or 90% score.

That said, my key takeaway from this is that professors are very impressed by perfect scores irrespective of how you got them. There must be something magical about that row of 100s. Once you set up a grading/ranking system, it is psychologically very hard not to admire people who end up at the top.

I am personally a little dubious that the people with the highest scores would make the best pool of employees, especially given that this is an online course without the programming component, but what do I know?

I wrote Java code for most of the algorithms in AIMA as a side project a few years ago [1], and after I read an online post by Peter Norvig saying a few of his students had tried and failed a few times (to implement the code in Java- Common Lisp code existed and the Python version was in its infancy), I sent him the code and this became the "official" java distribution for AIMA ( though I don't maintain it anymore- the immensely talented Ciaran O'Reilly of the Stanford Research Institute does) and no one ever invited me to Stanford or offered me a cool AI job[2], sob! :-P.

No I am not bitter I tell you, not even the teeniest bit :-p [3]

I wonder how this signalling will play into the upcoming courses? If there are tangential real world benefits to be gained by attempting a "perfect" score, then you can expect a lot more game playing wrt scores and exams.

[1] More about how Peter Norvig shredded my initial code etc here http://news.ycombinator.com/item?id=2405277

[2] though eventually, after a lot more work, it did lead to my working on good ML/robotics etc projects from Bangalore, which is a hard thing to do in the Great Outsourcing Wasteland.

[3] I am really not bitter.

I wrote the code for the hell of it, not to get a job. AIMA was my introduction to the fascinating field of AI. It is a great, great book and it has a lot more material than is covered in the course.

I once did want to go to Stanford and learn from the great profs there, but now in a "mountain comes to Mohammed" fashion, Stanford is coming to me. I don't care about the credentialling - I just want to learn. I took the AI online course and enjoyed Peter's and Sebastian's teaching immensely. Fwiw I should have a high 90's score, (I didn't add it all up) but nowhere near a perfect score.

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[1] as well as Assignments/Midterm[2]). 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.

[1] http://www.youtube.com/results?search_query=machine+learning...

[2] http://cs229.stanford.edu/

"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.

> 229A is close to worthless

I'd hardly call it worthless myself. It lacks a deeper analysis of all the methods that are used, but using them can sometimes be a greater challenge.

I did the AI course and the ML course and find it a great way of getting a little overview of the subjects, so when I study on my own, I have a little direction.

The ML class is not worthless. It's not sufficient, but it's definitely not worthless. I've found it to be a great survey of the field.

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

> "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 :).

Now, it makes a lot more sense :)

While I agree with your general sentiment, note that at least in ML-class, you can resubmit quizzes as many times as you want for full credit. Thus over the course of an hour you can retake the quiz enough times to brute force a perfect score without breaking the letter (and arguably the spirit) of the honor code.

Another interesting aspect of this "online ethics" is that there's no technical measures preventing you from pulling up past quiz results, and even if there was, you could still keep previous sessions open in a browser and even if there were countermeasures against that (javascript erasing), JS could be disabled, etc.

The Programming Projects that ML class had were slightly better metric of performance as there's more work that would have to be plagiarized, and if you're just going to go through life outsourcing all of your work then I guess that's your prerogative. However, I think that if you wanted to be very serious about actually testing for knowledge of material then the addition of some sort of interview component (phone/skype session), while time-consuming, could help.

In a way, yes, the programming projects in ML seemed like a better measure of performance in that you have to actually figure something out. However, they have two (sort of) disadvantages vs normal homework:

1) you immediately know if you got it right or wrong when you submit, so you can to a lesser extent brute force the correct answer

2) with the exception of maybe the first assignment, they are all "fill in the blank" sort of programming assignments. You basically just have to find the equations they give you in the PDF, translate them directly to Octave, and bam you're done.

I can't comment on online courses, but in general there is a HUGE difference between people who get As and people who get 100% on every single assignment. Never making a single error is an amazing feat.

I personally have only scored straight-100%s in a single course (Python programming), and that was only because I was relatively an expert in the material before the course began.

If you are getting 100's on everything it means you are gaming the spirit of the learning, overfitting the memory. Plop that guy in front of a computer with specs and a deadline and you will learn why grades are not an indicator of success.

(Somewhat-smart-ass response alert!)

Well, the only two people I personally know who would get all 100s are Peter Norvig and Sebastian Thrun, and I personally wouldn't mind hiring them!

Of course, in reality, Peter Norvig and Sebastian Thrun are working on projects that have long time horizons, e.g. self-driving cars and search. So perhaps you're still correct: The people you would hire to bang out code to meet a short deadline are probably different from the people you would want to work on your long-term technology bets.

In general, I disagree that knowing a topic incredibly well is necessarily overfitting. Deep knowledge can only aid new insights. You often hear about mathematicians and physicists who think by inhabiting their own mental world, composed of insights that they hold so deeply that they are _intuitive_.

Especially considering how many of those questions ask you to guess. These people either already knew the material, cheated or were really effing lucky.

The work you did strikes me as a far better measure of character and subject understanding.

I know a few people (who shall remain nameless) who collaborate and check each others

Why would someone do this for a free online course that gives no credit for a degree? I mean, the whole point is to learn, not get the highest grade. I really fail to understand people sometimes.

Its possible that to them, the scores aren't important and its a more conducive (and realistic) learning environment if they work together to solve the problems rather than doing it alone. There are many advantages:

* rather than just giving up on a problem, you can talk it out and learn together * you get the opportunity to teach the material that you think you know that others find hard (a good heuristic for problems you may have just barely understood, but gotten correct anyway). Teaching material is a great way to learn it, and expose any gaps you might have in your knowledge. * instant feedback on problems while they are still fresh in your memory

Because they suspected that there might be value in getting great scores? (As has been proven true?)

Because of this:

Your final score will be calculated as 30% of the score on the top 6 of your 8 homework assignments, 30% your score on the midterm exam, and 40% your score on the final exam. For those completing the advanced track you will receive your final score as a percentage as well as your percentile ranking within all those who completed the advanced track, and this will appear on your statement of accomplishment. The statement of accomplishment will be sent via e-mail and signed by Sebastian Thrun and Peter Norvig. We hope to have them digitally signed to verify their authenticity. It will not be issued by Stanford University.

Why do people keep coming back to games like farmville and games are now rampart with side achievements with little point like collect 300 x in zone y? I think some people on some level would feel the need to use all means available to maximise scores purely as something that needs to be checked off like achievements in games.

People form study groups all the time, it's a great way to learn. Since it's a free online course all of the benefit is what you actually learn, there is no way to cheat. At least that's my point of view (I am not in any of the classes, but wouldn't hesitate to co-work on stuff if I was).

Homeworks must be completed individually, and while we encourage students to help each other learn, homework assignments must be your own work and not done with a group.

As with the homeworks, exams must be completed individually without the help of other people.


I think that lots of people probably did collaborate on the homeworks and the mid-term and likely will do so again on the final and it was and will be cheating.

It's a shame, especially given that the instructors do seem to be attaching some importance to students' scores and rankings, but I'm not letting it detract too much from my enjoyment of the class.

Again, maybe it's just me, but this is a free online course that everyone is doing for their own knowledge. There's no degree being granted and it doesn't count for anything. I wouldn't bother to read the rules and certainly not attempt to follow them. I would try and learn the material as best I could, however that is.

Bragging rights?

Force of habit?

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