So, I've been trying to learn Machine Learning. It's been an open subject for me for years; I remember going to university to learn computer science (which took me a long time to begin with -- a story for another day perhaps) already with the fuzzy idea of going into Machine Learning.

Prior to that I had studied Literature/Linguistics, and already I had the fuzzy idea there of specializing in Neurolinguistics. In the end I only did some introductory subjects on the matter before dropping out. Dropping out is sort of what I do, by the way. I'm trying to change this aspect of my personality, and actually starting this blog has to do with that.

Anyway, I've always been interested in ML, but for several years now I've been content with reading Wikipedia on it from time to time. And to sit back and sort of spectate about how the field has been advancing. Only a month ago I decided to get into it more seriously, and I've been doing the well-rated (and by now sort of classic) Coursera course on the matter (Andrew Ng's). It's been great fun, and I intend to do it to completion (not to drop out this time). What comes after that, I'm not completely sure right now, but that's sort of exciting on its own right.

machine learning

• a [[field]]
  • of [[study]]
  • [[go]] https://en.wikipedia.org/wiki/Machine_learning pull
    • "computer algorithms that can improve automatically through experience and by the use of data"

Machine Learning

Go to [[Week 2 - Introduction]] or back to the [[Main AI Page]]

**Machine Learning, a subset of AI, uses computer algorithms to analyze data and make intelligent decisions based on what it has learned. **

Uses models not rules.

A solid reference I've used heavily is Models for machine learning from the IBM developer blog.

Statistical Algorithms

Machine Learning Models

The three types of machine learning

[[Applications for the three types of machine learning]]

machine learning