# Blog

## Stanford’s Machine Learning week 1 - What I’ve been doing?!

Oct 02, 2014 | 5 minutes read

This was my feeling during the first 7-minutes video lecture.

I’ve been listening about machine learning and AI for years as a remarkable thing, still being developed by researchers in the best universities of the world and to aimed in a PhD. Still with this idea, I decided to attend Stanford’s Machine Learning course offered on Coursera.

For me, one of its popular definitions reforces this idea of being something magical:

Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.

Arthur Samuel (1959)

Knowing enough about machines, I used to ask myself: “How can a computer do something I haven’t explicitly programmed?”. The answer is (at least in the scenario presented by Andrew Ng in the course): it doesn’t. Even without fully understanding all the models presented, I can already come up with probably not so efficient but similar solutions to the same problems.

Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Tom Mitchell (1998)

Putting aside the letters which may cause some trouble on understanding it, defines Machine Learning as something more tangible to programmers:

Recording and analyzing results from predefined tasks, the software will group them into satisfactory or not. Satisfactory may suffer another analysis attempting to improve and reach the performance expected.

Irio Musskopf (2014)

Andrew classifies Machine Learning algorithms between supervised and unsupervised learning. Both need to be fed with data to be analyzed.

## Supervised Learning

It’s when we give right answers together with the data. One of its examples - and the most explored in this week - is the housing price prediction. Based on a database of house characteristics (starting with size in feet), the algorithm should try to answer the question of how much a given house is worth.

## Unsupervised Learning

You may as well feed an algorithm with data and expect it to be grouped in some way. I don’t know how, but I want to split it between meaningful groups. It’s the same learning used in applications like Google News to populate a “related articles” section. Or put it into “business” and not “sports” category.

So far, this kind of learning wasn’t well explored yet, but the Eureka! moment stays with the possibilities. Tagging articles are useful - and may be helpful for unsupervised machine learning - but common people does not know how to use and isn’t a considerable choice for systems with size of those from Google. For the majority of cases, a computer could do a better (considering cost-benefit) job compared to a human.