Our imaginations have long been captivated by visions of machines that can learn and imitate human intelligence. While machines capable of general artificial intelligence-like Arthur C. Clarke's HAL and Isaac Asimov's Sonny-have yet to be realized, software programs that can acquire new knowledge and skills through experience are becoming increasingly common. We use such machine learning programs to discover new music that we might enjoy, and to find exactly the shoes we want to purchase online. Machine learning programs allow us to dictate commands to our smart phones, and allow our thermostats to set their own temperatures. Machine learning programs can decipher sloppily-written mailing addresses better than humans, and can guard credit cards from fraud more vigilantly. From investigating new medicines to estimating the page views for versions of a headline, machine learning software is becoming central to many industries. Machine learning has even encroached on activities that have long been considered uniquely human, such as writing the sports column recapping the Duke basketball team's loss to UNC.
Machine learning is the design and study of software artifacts that use past experience to inform future decisions; machine learning is the study of programs that learn from data. The fundamental goal of machine learning is to generalize, or to induce an unknown rule from examples of the rule's application. The canonical example of machine learning is spam filtering. By observing thousands of emails that have been previously labeled as either spam or ham, spam filters learn to classify new messages. Arthur Samuel, a computer scientist who pioneered the study of artificial intelligence, said that machine learning is the "study that gives computers the ability to learn without being explicitly programmed". Throughout the 1950s and 1960s, Samuel developed programs that played checkers. While the rules of checkers are simple, complex strategies are required to defeat skilled opponents. Samuel never explicitly programmed these strategies, but through the experience of playing thousands of games, the program learned complex behaviors that allowed it to beat many human opponents.
A popular quote from computer scientist Tom Mitchell defines machine learning more formally: "A program can be said to learn from experience 'E' with respect to some class of tasks 'T' and performance measure 'P', if its performance at tasks in 'T', as measured by 'P', improves with experience 'E'." For example, assume that you have a collection of pictures. Each picture depicts either a dog or a cat. A task could be sorting the pictures into separate collections of dog and cat photos. A program could learn to perform this task by observing pictures that have already been sorted, and it could evaluate its performance by calculating the percentage of correctly classified pictures.
We will use Mitchell's definition of machine learning to organize this chapter. First, we will discuss types of experience, including supervised learning and unsupervised learning. Next, we will discuss common tasks that can be performed by machine learning systems. Finally, we will discuss performance measures that can be used to assess machine learning systems.
