Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)

MIT presents a concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars. No in-depth knowledge of math or programming required! Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers: • The evolution of machine learning• Important learning algorithms and example applications• Using machine learning algorithms for pattern recognition• Artificial neural networks inspired by the human brain• Algorithms that learn associations between instances• Reinforcement learning• Transparency, explainability, and fairness in machine learning• The ethical and legal implicates of data-based decision making A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi. A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of "the new AI." This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin, author of a popular textbook on machine learning, explains that as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition?as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of ?the new AI.? This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.00Alpaydin, author of a popular textbook on machine learning, explains that as ?Big Data? has gotten bigger, the theory of machine learning?the foundation of efforts to process that data into knowledge?has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data Series Forward Preface Why We Are Interested in Machine Learning Machine learning, Statistics, and Data Analytics Pattern Recognition Neural Networks and Deep Learning Learning Clusters and Recommendations Learning to Take Action Challenges and Risks Where Do We Go from Here? Glossary Notes References Further Reading Index "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Provided by publisher A concise overview of how computer programs can learn from data, what they can learn from data, and what happens after that. Some of the biggest transformations in our lives in the last half century are due to computing and digital technology.