Machine Learning

From Wikipedia:

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.

Machine learning algorithms use data to learn how to perform a task, rather than programmed rules. As a field, machine learning is quite diverse, pulling from several topics such as linear algebra, probability theory, and calculus. The family of machine learning algorithms is also quite diverse, but there are a few categories that are used to keep track of all of them:

Supervised learning

Regression
Classification

Unsupervised learning

Clustering
Dimensionality reduction

To use machine learning effectively, you need a lot of different skills. You have to understand how machine learning algorithms work, but you also have to understand how to work with large amounts of data. If you don’t want to reinvent the wheel, you’ll want ot learn how to use machine learning software. And if you want to be able to process more than a few gigabytes of data in a reasonable amount of time, you’ll want to learn how to use high-performance computing (HPC) systems. If you are hoping to go into industry, I highly recommend that you try to learn about the kinds of skills that are valuable as a machine learning engineer or data scientist (this article is a good start).

Further Study

This page is only an introduction to what there is to learn about machine learning. If you really want to get involved with this field, you have a lot of studying to do. Below are some educational resources for machine learning, including courses and papers:

And some blogs and articles:

And some podcasts:

And some cool interactive things. Check them out!