2005 SLOC Laumeier

With no particular reason to favor one report over another this week, I just went with confluence of dates. This week's off-day throwback race report is from exactly 11 years ago.

Run May 15, 2005.

The SLOC picnic has been a bit of a wash the last few years. Two years ago, it was literally washed out by an absurdly strong thunderstorm. Last year, the publicity went out too late and not many people brought food. This year, everything went well and we had a really nice picnic on a beautiful day in a tiny, but fun park.

Laumeier is a sculpture park. The park has a dense trail network connecting several dozen sculptures. Most of the sculptures are quite large and many are huge. The combination of the unusual features and contrasting vegetation (half fields, half thick woods) makes for a pretty unique orienteering experience.

The course was a 22-control Score-O set by Mark Geldemier. Although the only overall route decision was which direction to run the loop and which order to take 16 and 18, Mark did an excellent job of providing many route choice legs. Such legs are particularly hard to evaluate when you're punching a control every minute.

I won with a time of 22:19, with David Frei coming in second at 23:18. Jeff Sona got all but one control [probably coming in fourth, as my map notes have Rick Armstrong listed as third, but the original report just listed the Carol's Team participants]. Yvonne Deyo ran with her husband. After eating more chips and cookies than we probably should have, we all went over to the Meramec River and paddled for a couple hours.

Quick thoughts on Data Mining

Following my format from last semester, here's some quick hits on Data Mining with a more thorough recap to follow.

I went into the class not sure how much I would learn. After all, it's not that far off from what I do every day at work. Turns out I learned a lot. Probably more than any other course I've taken since Cornell. So, why am I not happy?

Honestly, I don't know. Maybe I'm just being a jerk. I won't dismiss that, but let's shelve it for the moment.

The lectures were super dumbed-down. Basic probability and linear algebra were pre-requisites. While I certainly get that a prof doesn't want to dust half the class, I don't think there's anything wrong with telling the students that if they don't remember a certain fact from a prereq course, they can just dig out their old text and if they still don't get it, bring it to office hours. The students who do remember the stuff (or, as in my case, the students who went to considerable effort over winter break to dust that stuff off) would rather the class time be spent on the actual subject matter at hand.

We got almost no feedback on our assignments and tests until after the final. That's total bogus. Even after the final, all we got were numeric scores. That's not particularly useful even before the final. It's completely useless after.

As with the other two CS courses I've taken at UMSL, the focus was way too applied for graduate level work. I'm beginning to wonder how anybody writes a credible dissertation in this department. It's certainly not inspired by coursework.

OK, that's all true, but the fact remains that I really did learn a lot (albeit, mainly from the text and assigned papers). And, while I don't think that getting an A in this course constitutes as particularly strong academic achievement, I obviously received no injustice in the grading. So, I should probably stop being pissed about it.

I will. I'm pretty good at just moving on. But, while tuition at UMSL is ridiculously cheap for in-state students such as myself, these courses do represent a significant investment of time and there's nothing cheap about that. I think they could do better and I think they should.

That said, I'm basically done with traditional coursework at this point. Pretty much everything going forward will be directed readings or dissertation research. So, it's certainly not worth getting worked up over.

And, yes, maybe I'm just being a jerk.

Uninformed

I had intended to use a non-informative prior on the upper bound of the Uniform distribution that the block sum is drawn from. A few problems immediately present themselves.

The best non-informative prior for Uniform is the Jeffreys prior (or reference prior; they're the same in the univariate case) p(U) = 1/U, where U is the upper bound of the distribution.

The first obvious issue is that this is an improper prior. Not only does it not integrate to 1, it doesn't integrate to anything. That means that, until I have some data, I can't estimate a mean which means I can't estimate a variance which means I can't determine whether I should be sampling this stratum.

That's not terribly difficult to work around. Just set U = nb_k until we have some data or, cap it at nb_k (since it can't possibly be larger than that) which makes the integral finite.

The bigger problem is what happens after the data arrives. Given a block sum of X, the posterior is p(U|X) = X / U ², U > X. That's a perfectly good density function, but it has rather atrocious consequences. Namely, if that first block sum is small, it's going to drive the estimate for all remaining block sums way down and crush the estimate of the variance in the process. As such, we won't return to the stratum to sample more blocks and find that the sums are generally much higher.

So, while there is no way to know what the distribution of U is when starting a query, the non-informative approach is going to kill the algorithm. Therefore, I have to inject a fake belief that the sums are higher and bake that into the prior. This is essentially setting the prior consistent with an "assume the worst" attitude.

I think that's defensible in principle but leaves me without any mathematical precedent on which to pick a prior. So, I guess I'll just run a bunch of empirical tests and try to find a some sort of consistent shape. Or, at the very least, some starting point that results in posteriors that have that can represent a family of shapes observed.

All's well that ends well

Not sure if it was generous grading or a big curve (probably both), but I wound up with an A on the Data Mining final. Grades for all the other work in the class were also published. As I expected, they were also A's, so the 4.0 stays in tact for now. Seems like if all that stuff could have been graded in the past two days, it could have been graded last week as well when it would have served a purpose. Ah, well, that's all I'll say on that.

I've also turned in my final project for Bayesian Stats even though it's not due until tomorrow. I'm going to spend the rest of this week catching up at work and then really go heads down on getting a decent draft of my CISS paper done. There's actually a bit more research I want to conduct on that front. Hopefully it will only take a few days. Basically, what I've found from messing around with it is that it works great until the percentage of rows hit within each block gets really low. Then the prior of Uniform(0,max possible) on the block total starts to mess up the estimate of the variance. So, I need to change that to a hierarchical model where the block sum is distrubuted U(0,X) and X is distributed via something else.

Not sure what that something else should be. Obviously, I don't want to pull it away from max possible too quickly or it will mess up the convergence of the bigger queries which are currently behaving quite nicely. And, of course, since we can't be running MCMC chains during query processing to get posteriors, it needs to be something with a tractable conjugate distribution.

Train wreck!

That didn't go particularly well. As I have no feedback from other assignments, I'm only left to guess if my average in Data Mining coming into the final was good enough to survive that mess. I'm pretty sure it is but it's still really a bummer. Spent nearly half the time on this convoluted conditional probability problem that had absolutely nothing to do with data mining. Overall, it was a good class, but these last few weeks have been exceedingly depressing. Just when we got to the interesting stuff, the course nose-dived into rudimentary calculation (which is definitely NOT what I'm any good at, nor do I care to be; that's what computers are for).

I'll wait a couple days before doing the course eval because I know I wouldn't give it a fair shake right now.

Last call

Later today, I'll be turning in my final project for Bayesian Stats and sitting for my exam in Data Mining. After that, I'm going out drinking with my wine club, so this is all the post you're going to get today.

Feedback loop

So, tomorrow is the final for Data Mining. I'm not terribly worried about it, but if I was even a little worried about it, I'd be a lot worried about it. Simply put, we've had no substantive feedback.

We've taken two exams, submitted three assignments, and presented a paper. We've got the first exam and assignment back. That is outrageous. The whole point of grading is to give feedback so students can adjust. There simply has been no opportunity to do that in this course.

Unfortunately, this appears to be a social norm at UMSL. I generally don't take pot shots at a school to which I'm attaching myself, but this really needs to be called out. I've now taken four classes at UMSL and the average time from when an assignment or test is collected to when feedback is given is around 3-4 weeks. That's pretty useless. By the time you're four weeks behind, you're dead.

I'm not really sure how to lodge the complaint. The course evaluation is the obvious place but, again, this appears to be a problem of culture, not just one or two profs being delinquent. I never put it to the test, but I'm quite sure that if I had sat on grading for four weeks at Mount Union, I would have had a personal and not particularly pleasant conversation with the dean. Of course, Mount Union also charges ten times as much for tuition, so the students have a legitimate gripe if they're getting anything less than stellar service.

However, just because the courses are state subsidized doesn't mean that grading isn't important. I think this point is particularly salient in a Data Mining class where all the machine learning algorithms we're studying are predicated on fast and accurate feedback.

Grading sucks. It's by far the least fun part of teaching. It's also vitally important. Every job has things that you simply have to do whether you like it or not. This is one of them.