- Machine learning allows a computer to teach itself how to solve problems by analyzing large sets of data.
- Human programmers don’t teach machine learning systems how to solve problems, nor do they generally understand the algorithm that a computer devises based on the data.
- Here is a brief introduction to machine learning and common uses for the technology.
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Machine learning is a fast-growing and successful branch of artificial intelligence. In essence, machine learning is the process of allowing a computer system to teach itself how to perform complex tasks by analyzing large sets of data, rather than being explicitly programmed with a particular algorithm or solution.
In this way, machine learning enables a computer to learn how to perform a task on its own and to continue to optimize its approach over time, without direct human input.
In other words, it’s the computer that is creating the algorithm, not the programmers, and often these algorithms are sufficiently complicated that programmers can’t explain how the computer is solving the problem. Humans can’t trace the computer’s logic from beginning to end; they can only determine if it’s finding the right solution to the assigned problem, which is output as a “prediction.”
Types of machine learning
There are several different approaches to training expert systems that rely on machine learning, specifically “deep” learning that functions through the processing of computational nodes. Here are the most common forms:
Supervised learning is a model in which computers are given data that has already been structured by humans. For example, computers can learn from databases and spreadsheets in which the data has already been organized, such as financial data or geographic observations recorded by satellites.
Unsupervised learning uses databases that are mostly or entirely unstructured. This is common in situations where the data is collected in a way that humans can’t easily organize or structure it. A common example of unstructured learning is spam detection, in which a computer is given access to enormous quantities of emails and it learns on its own to distinguish between wanted and unwanted mail.
Reinforcement learning is when humans monitor the output of the computer system and help guide it toward the optimal solution through trial and error. One way to visualize reinforcement learning is to view the algorithm as being “rewarded” for achieving the best outcome, which helps it determine how to interpret its data more accurately.
Applications for machine learning
The field of machine learning is very active right now, with many common applications in business, academia, and industry. Here are a few representative examples:
Recommendation engines use machine learning to learn from previous choices people have made. For example, machine learning is commonly used in software like video streaming services to suggest movies or TV shows that users might want to watch based on previous viewing choices, as well as “you might also like” recommendations on retail sites.
Banks and insurance companies
Banks and insurance companies rely on machine learning to detect and prevent fraud through subtle signals of strange behavior and unexpected transactions. Traditional methods for flagging suspicious activity are usually very rigid and rules-based, which can miss new and unexpected patterns, while also overwhelming investigators with false positives. Machine learning algorithms can be trained with real-world fraud data, allowing the system to classify suspicious fraud cases far more accurately.
Inventory optimization – a part of the retail workflow – is increasingly performed by systems trained with machine learning. Machine learning systems can analyze vast quantities of sales and inventory data to find patterns that elude human inventory planners. These computer systems can make more accurate probability forecasting for customer demand.
Machine automation increasingly relies on machine learning. For example, self-driving car technology is deeply indebted to machine learning algorithms for the ability to detect objects on the road, classify those objects, and make accurate predictions about their potential movement and behavior.