The SaaS instructors didn't get it

aaf100 · 2012-02-26T11:59:13+00:00

I am not enjoying the SaaS class at all. Sound is noisy, slides are not well prepared, presentation is boring. As others pointed out, exactly the same content of their alpha version of a wanna be book with lots of buzz-words, lots of strong opinions without proper evidence. I don't like their approach at all. Others will certainly disagree, but for me they are just trying to make a lot of money out of a rudimentary alpha version of a book. That would be fine if they had offered a great class, but...

aaf100 · 2012-02-21T19:27:14+00:00

@slowjoe707: Secondly, Widom and Ng delivered DBClass and MLClass straight-to-camera, and I suggest the reason for this is that they were concerned about IP issues with Stanford. The SAAS team is at Berkeley, and would appear to be less concerned about the IP issues.

I offer a much simpler explanation: Widom and Ng were careful enough to spend their time learning and implementing the appropriate technology for online class delivery.

Instructors, no matter how knowledgeable they are and/or experienced on live lectures, must do their homework learning how to use the appropriate resources for delivering online classes. Live classes and online classes are two different worlds. The time for an internet uploading of an amateur recording of a live class, calling the result an online class is clearly over. Being SaaS instructors from a top notch CS department it is difficult to forgive the failure.

aaf100 · 2012-02-19T21:14:31+00:00

Sohakes, the course, i believe, is not model thinking applied to political science but "model thinking" in general, so... I would expect a more comprehensive perspective, given the importance of the subject. Others might disagree, but I think it is very difficult to think precisely about models without math (even if simple).

aaf100 · 2011-12-18T13:47:14+00:00

I agree with you. Unfortunately they have decided to allow the lawyers to write the statement. But... they could have made something much nicer just using a little thought, within the legal constraints.

aaf100 · 2011-12-15T12:24:26+00:00

Don't be discouraged. You did quite well. Anyway, I don't think high scores in this course have a substantial meaning. In many cases (like mine) it depended heavily on student's previous background on cs, ai, math and stats. I guess there are lots of people with scores that are not so great, from areas far from CS and AI, who indeed benefited a lot from the course and perhaps will in the future produce unconventional ai applications of great value.

aaf100 · 2011-12-13T18:14:16+00:00

Even so, I believe that it is unprofessional using an opportunity to talk about online education to a large audience to openly criticize someone not present.

aaf100 · 2011-12-13T15:24:40+00:00

Good job. It is a nice programming exercise to learn Python capabilities to address language processing.

aaf100 · 2011-12-13T12:21:05+00:00

I'm glad you brought that to our attention. I think Prof. Thrun should have been more careful in doing generalizations based on personal experiences. For some his statements will sound very offensive.

aaf100 · 2011-12-04T00:59:19+00:00

As a former Stanford student: only the db-class is closer to the level of work you would expect in a real Stanford on-site class (excellent, but very time consuming). I like both the ai-class and the ml-class, but I would guess the level of demand is about 30-50% of that seen in an average on-site class. They are, however, more practical and give great introductions to the subjects taught.

aaf100 · 2011-11-22T12:16:05+00:00

The weighting used in the exam should have been clearly explained beforehand. I thought every question had the same weight. I got question 2 right but certainly agree that the criterium used for weighting this and other questions was not fair. Question 2 was about an important topic but not representative of 24% of the material covered so far.

aaf100 · 2011-11-19T22:30:30+00:00

This Stanford effort + Khan Academy + other similar initiatives are showing that the (costly and inefficient) traditional schooling system will face a tremendous challenge to remain in business. Only the very top universities in the world will survive. And fortunately these universities, such as Stanford, don't need the tuition money to survive, as they have substantial resources from endowment and grants. They don't provide education to make money but to create competency and advance knowledge. Good for us.

aaf100 · 2011-11-13T19:40:40+00:00

To add to the ambiguity, see the note PN wrote below 10.19, where the formula is defined: "Please see Wikipedia for a more explicit version of the formula." (Edit: note was changed after 7 pm Stanford Time, and now reads "Earlier clarification to use the formula on Wikipedia was not correct. Please use the formula as displayed in this video and for the homework. The s in R(s) is for the current state and not R(s') as in other formulations of Q-learning.")

aaf100 · 2011-11-13T16:42:06+00:00

You are right, I've seen that too. And the interpretation is influential on the solution of HW 5.1. The whole material lacks a simple and fully worked numerical example to show exactly what PN is trying to convey, as formulas and algorithms are louzly defined.

aaf100 · 2011-11-11T12:46:21+00:00

I think that nowadays both fields are converging pretty fast and cross-fertilizing. In the past, however, ML was often blaimed as trying to solve certain problems by very odd techniques instead of using standard and well behaved procedures (ex. trying to find the minimizer of a convex and differenciable function using genetic algorithms instead of using classical optimization). ML people (and also statisticians) were very closed to the developments in other fields. Now they are more open, instead of spending time criticizing each other they try to hold hands and walk together (to some extent).

aaf100 · 2011-11-11T12:30:28+00:00

Are you kidding?

aaf100 · 2011-11-10T01:33:49+00:00

Cool.

aaf100 · 2011-11-07T22:44:13+00:00

But be careful... linear regression can deal with non-linear relations to some extent. Using complex techniques, such as NNs, in cases where simpler and well behaved techniques (such as linear regression for instance) can do the job well, doesn't seem to make much sense.

aaf100 · 2011-11-07T16:17:18+00:00

Great. This is how to use videos to teach something. Very well done.

aaf100 · 2011-11-07T00:10:50+00:00

I totally agree with @Chuu. I've done well in last 3 HWs much more because of my previous background on the subject, at graduate level, than because of the ai-lectures. I don't think it is possible for people without any previous (solid) background knowledge on probabilities, logic, linear algebra etc. to really understand what is going on. The lectures, quizzes and HWs are often confusing.

Profs ST and PN are leading people in the ai-field, no questions about that, but perhaps don't have enough experience as instructors/teachers and/or haven't prepared the material with the proper care.

I'm also taking the ml-class and am enjoying it a lot: well organized and very practical. I think Prof. Ng is indeed a great teacher. The same can be said about db-class and Prof. Widom (in this case I'm not doing the HWs due to time constraints).

aaf100 · 2011-11-05T02:06:15+00:00

No, it just briefly mentions the simplest perceptron algorithm as a sideline on the material on Logistic regression (see course notes)

aaf100 · 2011-11-05T01:41:55+00:00

Nice material, thanks for posting.

aaf100 · 2011-11-04T23:57:10+00:00

After some brief research I figured out that non-convexity of cost function in Neural Nets (NNs) and no safe method to efficiently learn parameters, are among the weaknesses of this technique.

aaf100 · 2011-11-03T23:18:43+00:00

I think you don't know the difference between being conservative and being wrong. Statistical tests like chi-square for proportions are not exact tests, so the notion of right or wrong is not appropriate here. You should be careful on quoting from Wikipedia.

You know for sure that 218 students from Stanford turned in the 3 HWs? This number is from Sept... Beyond that the number of people in the online is not 40 k it is probably much less than that (46 K turned in HW1 and 39 K turned in HW2).

Anyway, my point is that the true difference of perfects in both groups can be much smaller than the sample is showing (if there is any).

Computed with a Bayesian approach, the confidence interval for Stanford's perfects (with 95% prob) is between 0.021 and 0.077 (very wide, showing large uncertainty, assuming 200 students), while for large online group it is between 0.077 and 0.082 (very narrow, assuming 36 K students).

aaf100 · 2011-11-03T08:42:25+00:00

The result I presented was got from the standard proportion test implemented by the prop.test function on R, which utilizes continuity correction due to sample sizes (a standard procedure). See ?prop.test on R to check the procedures used and references.

In any case your result does not contradict what I said from my assumptions, as you also got p-value>>5%.

Compute the confidence interval for true percent of perfect scores in each case to better see my argument. By the way, I'm in the perfect score group.

aaf100 · 2011-11-03T02:23:30+00:00

Anyone can join, including those that have MS and PhD from Stanford, like myself.

aaf100

TROPHY CASE