Most machine learning problems belong to one of the following three main categories:
- In supervised learning, each data point is labeled or associated with a category or value of interest (Chapter 3, First Steps in Supervised Learning). An example of a categorical label is assigning an image as either a cat or dog. An example of a value label is the sale price associated with a used car. The goal of supervised learning is to study many labeled examples like these (called training data) in order to make predictions about future data points (called test data). These predictions come in two flavors, such as identifying new photos with the correct animal (called a classification problem) or assigning accurate sale prices to other used cars (called a regression problem). Don't worry if this seems a little over your head for now--we will have the entirety of the book to nail down the details.
- In unsupervised learning, data points have no labels associated with them (Chapter 8, Discovering Hidden Structures with Unsupervised Learning). Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. This can mean grouping them into clusters or finding different ways of looking at complex data so that they appear simpler.
- In reinforcement learning, the algorithm gets to choose an action in response to each data point. It is a common approach in robotics, where the set of sensor readings at one point in time is a data point and the algorithm must choose the robot's next action. It's also a natural fit for Internet of Things applications, where the learning algorithm receives a reward signal at a short time into the future, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward.
These three main categories are illustrated in the following figure:
Main machine learning categories
