Machine Learning Approaches

Machine learning is probably the most important field within modern artificial intelligence. As its name implies, it aims to provide machines the ability to learn. More specifically, it concerns itself with algorithms that can automatically learn models and patterns from data, without being explicitly told where to look.

Model building

In many applications, we are required to build models: representations of something else (e.g. a process or system) based on general rules, concepts or principles. Models can serve many purposes – for example, they can allow us to gain a better understanding of how some system works. Also, they might allow us to make predictions about the things they represent – and using these predictions, they might tell us how to take better actions.

For example, a model of the atmosphere allows us to make weather and climate forecasts. A robotic lawnmower will build a model of its environment so it can efficiently mow the lawn. Financial models allow traders to make better trades, etc.

Sometimes, it is easy to manually define rules and concepts in order to build a model that corresponds to a real-world system or phenomenon, or that explains the patterns in a certain dataset. In many cases though, this is a very difficult and labor intensive task. This is where machine learning comes in — in many of those cases, it offers a solution, by automating the task of building the model, with comparatively little instruction.

Different approaches

There are three main approaches of machine learning:

Getting started in machine learning

If you want to start learning about ML, head over to the section on supervised learning. It's a good place to start: it is easy to learn the basics, and with a base knowledge of supervised learning, you will be acquainted with many of the important concepts underlying modern AI, which you can then apply to more advanced topics like reinforcement learning.