Littler over 24 hours after the end of class, I am already feeling naked with so much time on hand. Chris Wilson induced a premature virtual nostalgia with his hilarious blog on ML class - here.
At Week 2 in Stanford’s machine learning course, the childishly simple homework assignments are growing more complex. Last week I wrote, “The great joy of learning new concepts in math and science is that they transport you into a simplified world, in which only a few things govern our lives and all problems can be solved—basically, a Richard Scarry book for the matriculating crowd.” In that case, we were making models of housing data based only on home size. Now we’ve got to worry about the number of floors and bathrooms, too. We’ve graduated from Richard Scarry to a more complex world that is beginning to get hairy.
So far, we’re still more occupied with student learning than machine instructing, though the path ahead is getting clearer. We’ve started learning a little programming in a language called Octave, which appears to be a graphing calculator on steroids, but it’s mostly to manipulate matrices, where we store data on all the values for the mathy part of this class. I understand that professor Ng has to introduce us into the really groundbreaking stuff gradually, once we have a strong command of the traditional ways that statistical modeling works, but right now I confess I feel more like I’m qualifying to be an actuary than a machine overlord.
My failure to complete the major, which I still regret, tuned me in to this peculiar divergence: You’re meant to be either a scientist or a fan of science, and you discover which one you are when the math gets rough.
The math is getting rough in this class. Matrix multiplication still twists my mind around, but it’s manageable with enough trial and error in the programming assignments. The larger lesson of the past week in Stanford’s machine learning course is that networks themselves possess a supreme intelligence, and developing one to treat your information well is not so different from allowing a computer to code itself. This is true of even very simple social networks in which no math need be applied.
The best analogy I have read in a long long time!!
This brings us to the “lazy hiker principle.” The real name for this technique is the “gradient descent algorithm,” but like I mentioned last week, the machine-learning professors could use a little help in marketing their material.
At Week 2 in Stanford’s machine learning course, the childishly simple homework assignments are growing more complex. Last week I wrote, “The great joy of learning new concepts in math and science is that they transport you into a simplified world, in which only a few things govern our lives and all problems can be solved—basically, a Richard Scarry book for the matriculating crowd.” In that case, we were making models of housing data based only on home size. Now we’ve got to worry about the number of floors and bathrooms, too. We’ve graduated from Richard Scarry to a more complex world that is beginning to get hairy.
So far, we’re still more occupied with student learning than machine instructing, though the path ahead is getting clearer. We’ve started learning a little programming in a language called Octave, which appears to be a graphing calculator on steroids, but it’s mostly to manipulate matrices, where we store data on all the values for the mathy part of this class. I understand that professor Ng has to introduce us into the really groundbreaking stuff gradually, once we have a strong command of the traditional ways that statistical modeling works, but right now I confess I feel more like I’m qualifying to be an actuary than a machine overlord.
My failure to complete the major, which I still regret, tuned me in to this peculiar divergence: You’re meant to be either a scientist or a fan of science, and you discover which one you are when the math gets rough.
The math is getting rough in this class. Matrix multiplication still twists my mind around, but it’s manageable with enough trial and error in the programming assignments. The larger lesson of the past week in Stanford’s machine learning course is that networks themselves possess a supreme intelligence, and developing one to treat your information well is not so different from allowing a computer to code itself. This is true of even very simple social networks in which no math need be applied.
The best analogy I have read in a long long time!!
This brings us to the “lazy hiker principle.” The real name for this technique is the “gradient descent algorithm,” but like I mentioned last week, the machine-learning professors could use a little help in marketing their material.
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