# Machine Learning: first impressions

Wow, this is pretty cool stuff. Some notes:

There are two kinds of machine learning: supervised and unsupervised.

Supervised learning is literally composed on of running regressions on datasets you know something about. That’s it. They further break the regressions down into binary variable regression (classification problems) and plain-old single/multi-variate regression. The point of regression, of course, is to estimate a smooth function that describes a lumpy dataset.

Unsupervised learning is where the sex is in this field. That’s google news (clustering stories together that are ‘about’ the same topic) and various other kinds of data mining. The idea is that you get a dataset and ask the computer to find a pattern.

There were quizzes in this, too, at which I did distinctly better than yesterday with the databases. I chalk that up to a better teacher setting questions up. As always, there are instances of false precision leading to a binary result (WRONG ANSWER, you idiot!), when in the real world I’d probably have gotten away with my approach.

For instance, there was a question that asked to identify the type of regression problem: one was predicting whether email was spam/not spam (obviously a ‘classification’/binary problem) and the other was to predict how many of a warehouse of goods would be sold or not sold in three months. I said that was also a classification problem, but the instructor thought it would be a normal regression problem. Could probably go either way, but I lose.

Finally, I am going to need to learn another programming language, Octave, which is apparently an open-source version of MatLab (The days of building programming languages and selling them for money are long gone.). Great.

Next up is a linear regression tutorial then a linear algebra lesson. I never took linear algebra, so I am distinctly not looking forward to the amount of time I’ll probably need to spend on this.

But I press on nonetheless.