Well. I survived last week's class and got 100% on the homework!! Part of this was due to a sudden realization that the demo code I had been so assiduously analyzing actually contained the skeleton of a system for answering three of the questions. And part was dumb luck, tempered with reason of course. The realization part happened after I had worked out the problems on my own, but I used the software to validate my answers -- which were, amazingly but truly, correct.
The original estimate for time to be spent on the class was a glib 1-10 hours a week -- it's not clear if that included watching all the video lessons which are at least 2 hours a shot -- and maybe some go-getter StanfooFrosh with all their god-given brain cells still intact could do it. Me? I'd say 50 hours last week trying to intuit the inner workings of Probability...
This week -- Machine Learning -- I got off easy. After only three days I believe I'm done. Or else I've missed something really important. Those days include time spent shuttling around finding working internet connections -- because my usually-fairly-almost reliable LCWireless coop took a big poop right after the videos were posted on Tuesday -- and summarizing the lessons for an online study group which meets Thursday evenings. In the course of the summary I discovered that I had developed a simplified method for working the hard parts of the homework (which I _might_ reveal after entries are closed next week). In keeping with standard practice only the first of the two lessons had any relevance to the homework. So now I'm living with the sneaking fear that the exams will cover the missing lessons.
As has been pointed out a number of times: Why do I care about my grade? I dunno. Knee jerk reaction to jerks I guess.
Moving on to the philosophy portion of our time here together....One thing I've noticed about the class so far is that it makes heavy use of exactly what computers are good at: Mindless Iteration.
First we had Search which is just opening doors and walking down hallways until you stumble upon that which you were seeking. Admittedly there are some shortcuts. And even some automated ways to discover the shortcuts. But it's really just wandering around in a big field without your glasses.
Then there was my bugaboo, Probability. This boils down to multiplying and adding big lists of small numbers. Over and over. It's something that Professor Sebastian seems to pride himself on being able to do, but god help me, that's why we have computers isn't it? Of course one does need to be able to set up the problem and understand the necessary transformations -- and the results, which are in many cases "not obvious" -- but that's Systems Analysis.
And this week, Machine Learning. Many of the problems presented make big use of Probability so it goes without saying that there's a lot of repeated number crunching. Moving on to Regression and Clustering, to para-quote: "Often there are no closed form solutions so you have to use iteration." All manner of try-try-again-until-you-succeed perseverationist algorithms are put to use. Gradient Descent is just bumbling-around-in-a-field search with a proviso that one always bumbles down hill. And we haven't even addressed non-local minima yet.
So my question: Is this Intelligent behavior? In one respect, once a computer finds a way to do something we used to pride ourselves on, we always diss it by saying, "Well, that's not _really_ intelligent after all now is it?" But in another respect I think number-crunching may be going about it wrongly. In the map problem used to introduce different types of searching the question was how to get from Arad to Bucharest -- which is probably easier if you are in Romania A human would look at the map, squint their eyes for a couple seconds, and then go, "Yah shure, we gotta go through Rimnicu." The computer however tries all the possibilities...in the "less intelligent" versions it even goes the wrong direction, just to, you know, see...and then finally pretends that it has discovered a route.
What the computer does is wander around in the field until it trips on the solution, but what the human does is some kind of integration and abstraction of the data. I think this ability to Abstract is at the core of intelligence. We may get to some bits of that in this class but it's gonna be some rough iterations.
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