pbh

You're probably in a much better position to evaluate than I am! My guess is that more Octave-based classes would translate into more users and more code written for Octave down the line, but I'm not sure how to encourage more use of Octave in the classroom in the first place.

I've always been a bit sad about it, but everyone involved is probably a rational actor.

Computer science professors probably view a couple hundred dollars per MATLAB network license as a tiny expense on a $1m+ grant (whereas statistics grants are apparently often smaller), and they may be charged for it in departmental overhead anyway (removing the incentive to cut costs).

The type of people who could contribute either core code or toolbox type code to Octave often have an extremely rare quantitative skill set that is worth hundreds of dollars an hour, so there is a huge incentive to get paid to do similar work instead. There probably isn't much community recognition (to balance things out) for implementing a library in Octave. (Though, in the R world there are certain recognizable superstars like Hadley Wickham.)

Graduate students (who might work for cheap on these problems) are probably more focused on publications and networking.

As long as all of this is the case, Octave will always kind of just be a worse MATLAB that happens to be open source, so a new user choosing between them will probably just choose MATLAB by default.

This is a great description of R usage (import, clean, fit models), but I think a slightly erroneous explanation of R history.

John Chambers created "S" at Bell Labs. S was a programming language designed for interactive statistical analysis. Much like gcc and icc are implementations of C compilers, R and S-PLUS are implementations of S. S-PLUS was/is the primary proprietary implementation of the S language, whereas R is the primary free one (also, sometimes called GNU S). (SAS and SPSS are completely different languages/systems as far as I know.) I think that statisticians at some point made a conscious effort to publish their work in R, rather than S-PLUS (or any other statistical system like SAS) because it was more widely available. That in turn led R to be a viable competitor to S-PLUS (and other systems) because it had vast amounts of recent statistical libraries, often implemented by the people who developed the techniques. That said, SAS and SPSS seem to pretty much still have social science students locked up --- the market for R is probably statisticians who are also excellent functional programmers.

This history is in really marked contrast to MATLAB and its corresponding free version Octave, where computer scientists pretty much refuse to use Octave, despite MATLAB's massive price tag to pretty much everyone involved (even with 90% discounts).

(That said, if anyone lived through the change over from S-PLUS to R, I'd love to hear if this history is wrong!)

ezl does not seem to have gone all the way down the rabbit hole with the oDesk tracking link. (I work at oDesk, but not on this, so I was really curious.) After about 3 HTTP redirects, the www.dpbolvw.net link seems to turn into some sort of affiliate link. So my guess is that a (possibly errant, but presumably very data-driven) affiliate was the one sending him e-mail about oDesk, rather than oDesk itself.

That seems to raise a broader question, which is: to what extent should companies be blamed (and thus try to control) for the actions of the people in their affiliate programs? Would you be unhappy about a blog purporting to be by a cute ice cream eating girl filled with recommended books with Amazon affiliate links? What are the standards in this area, exactly? (On reload, see dabent's comment below as well.)

Actually, I thought his comment was quite delicate.

I used to think that DB comparisons were bad because of vested interests, like Oracle refusing to allow anyone to benchmark them. But I now think that Antirez's view is much closer to the truth: poorly informed people can't set up benchmarks correctly because they don't understand the underlying DB model and possible optimizations, and well informed people can't set up benchmarks because they can't think of a fair benchmark that doesn't explicitly handicap one model or set of optimizations versus the other.

(Of course, in this case, the comparison is pretty bad. Other commenters have noted that an inverted index is more sensible here and that Postgres' text search support should have been used. But the big issue to me is that the graphs are incredibly misleading, with totally different units on the y axis, so that a 0.004 second win for Mongo looks like as big a win as a 3 second win for PG.)

I think the most succinct response to this question I've found comes from the Starcraft II casting archon, "Tastosis" during one of those filler periods where nothing is happening in the game:

Tasteless: I'm a philosophy major, man.

Artosis: Hmmmm? That's good.

Tasteless: You major in that, people are like: "What are you going to do with a philosophy major?" ::outraged mumble:: I'm like: "I dunno, properly navigate the world with my mind? Geez!"

Is there a way to fix the recursion depth problem with this solution?

To me, the memoized version is more clear and should be just as efficient as the iterative version. However, this version of fib_mem dies before fib_mem(1000)!

Apparently, you can increase the recursion depth limit using the sys module. Is that the right thing to do? And, if you do it, are there guidelines for doing so without causing memory problems?

I know a lot of Daphne's former and current students and she seems to be a great advisor who genuinely cares about producing both excellent research and top quality research talent.

Further, in the department, I think she is one of the people who cares most about teaching. She runs the undergraduate summer research program. She re-does her class on PGMs substantially almost every time she teaches it to try to make it better. (Though such a high rate of change may or may not be a good idea.) Daphne is almost certainly one of the key people behind the *-class effort at Stanford CS.

For those with a negative impression of Daphne, my guess is just that they are misinterpreting her directness. If she thinks you're wrong, or you're doing the wrong research, you'll know about it.

(Also, Ullman is one of the nicest people in the department in person, which is crazy given that he wrote the standard texts in compilers, databases, and arguably automata. He's emeritus these days though.)

Is this a CVPR reference, or just an amazingly accurate comment?

Yes, I think computer vision is a common term for the area (at least, among academics and researchers), and CV is a common abbreviation.

Examples include Intel's OpenCV and the CVPR conference.

(I actually thought that it was weird that they called themselves "machine vision" rather than computer vision in the title.)

I agree with your point, but with an important caveat.

You are right that application developers should not be ignorant of databases, and DBAs should not be ignorant of what application developers are doing. All too often, this is the case.

However, the point when people came to think of application and database development as two separate things was the origin of databases themselves, and arguably one of the most important historical points in computer science itself. The notion that data has a life outside of any given application, and that it deserves itself to be managed, was and is incredibly powerful.

Unknown.

However, the three classes are intended to match up with Database Systems: The Complete Book (which, in the interests of full disclosure, is written partly by my former advisor). If you want to learn the material from the second class, I would just read the book. I've actually read the book cover to cover, and I think it's one of the best textbooks for that purpose. There are tons of examples everywhere, it's pretty talkative, and there is very little pretension (insert Russell/Norvig complaint here). On the other hand, some portion of the Internet seems to take the opposite view (e.g., some people just view the talkativeness as repetition).

To answer a few of the database questions that keep popping up in this thread:

1. Is it worth understanding relational algebra? Yes. Definitely. It's not that hard, there's more or less a one-to-one mapping of relational algebra to SQL. Relational algebra is both a useful mathematical tool and indispensable when trying to understand query optimization. Relational algebra is also necessary to be able to read pretty much any of the database literature, if that's one of your goals. (Just be thankful you don't have to learn Relational Calculus or Datalog.)

2. Is it worth learning XML features like XML DTDs, XPath, XML Schema, and XQuery? Yes, definitely. I don't like XML, but there are tons of places where you can use XPath. (The most interesting thing about XQuery is probably how similar it is to SQL, though.)

3. Is it worth learning about SQL triggers? Probably. Ultimately, the web development world is split between people for whom separation of concerns means doing more in the database, and those for whom separation of concerns means doing less in the database. If you think doing validation and transactions in the database is sensible, triggers are also a sensible thing to learn about. However, people like DHH disagree and want validation in your app server with a dumber backend.

4. Is this vocational training? Well, this is the first in a three class sequence at Stanford. This class is mostly about schema and query languages, so in some sense, it's the most useful to practitioners (or at least, non-DBA practitioners). The second class is about systems and implementation (on-disk layout, indexing structures, query optimization). In the third class, students either learn about distributed databases or actually build their own from scratch. However, schema and query languages have enough theory behind them, and enough generality, that I wouldn't consider it any more vocational training than a class on compilers.