Introduction

When studying Machine Learning you will come across many different terms such as artificial intelligence, machine learning, neural network, and deep learning. But what do these terms actually mean and how do they relate to each other?

Below we give a brief description of these terms:

Artificial Intelligence: A field of computer science that aims to make computers achieve human-style intelligence. There are many approaches to reaching this goal, including machine learning and deep learning.

• Machine Learning: A set of related techniques in which computers are trained to perform a particular task rather than by explicitly programming them.

• Neural Network: A construct in Machine Learning inspired by the network of neurons (nerve cells) in the biological brain. Neural networks are a fundamental part of deep learning, and will be covered in this course.

• Deep Learning: A subfield of machine learning that uses multi-layered neural networks. Often, “machine learning” and “deep learning” are used interchangeably.

Machine learning and deep learning also have many subfields, branches, and special techniques. A notable example of this diversity is the separation of Supervised Learning and Unsupervised Learning.

To over simplify — in supervised learning you know what you want to teach the computer, while unsupervised learning is about letting the computer figure out what can be learned. Supervised learning is the most common type of machine learning, and will be the focus of this course.