Machine learning patterns

I first heard of the Silicon Valley Patterns meetings from Alex Chafee a few years ago while participating in his “bootstrap” practice group. SVP sounded like fun, but I only got around to attending a meeting this spring, a one-off on led by Johannes Ernst (notes). I was going to write something about that meeting, but just can’t get worked up about digital identity.

SVP’s next extended track was on , a topic I have some interest in and very cursory knowledge of from reading popular books on AI. The track lasted from May through October. Mostly our study was guided by Andrew Moore’s statistical data mining tutorials, with occasional reference to Russell & Norvig.

I don’t think any of the regular attendees were machine learning experts, but with occasional contributions from everyone, I think everyone was able to increase their knowledge of the material. Overall a gratifying method of learning, though not a perfect substitute for lecture.

My secondary take way from the track was that I need a serious brush up on calculus and statistics, neither of which I’ve studied, and barely used, in fifteen years. I’m working on that.

The current SVP track should be very different–hands on Ruby on Rails practice. I’m attempting to justify putting in the time…

One Response

  1. […] I’ve briefly mentioned digital identity before, but the launch of ClaimID (more below) prompts me to write down my over the top theory concerning digital/online identity as a great absolute productivity equalizer. The theory in pseudocode: […]

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