Machine Learning, revised and updated edition
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MIT presents a concise primer on machine learningâcomputer 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!
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Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognitionâas well as some we donât 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 âthe new AI.â This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.
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Alpaydin explains that as Big Data has grown, the theory of machine learningâthe foundation of efforts to process that data into knowledgeâhas also advanced. He covers:
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âĸÂ The evolution of machine learning
âĸÂ Important learning algorithms and example applications
âĸÂ Using machine learning algorithms for pattern recognition
âĸÂ Artificial neural networks inspired by the human brain
âĸÂ Algorithms that learn associations between instances
âĸÂ Reinforcement learning
âĸÂ Transparency, explainability, and fairness in machine learning
âĸÂ The ethical and legal implicates of data-based decision making
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A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programmingâmaking it accessible for everyday readers and easily adoptable for classroom syllabi.
No in-depth knowledge of math or programming required!
Â
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognitionâas well as some we donât 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 âthe new AI.â This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.
Â
Alpaydin explains that as Big Data has grown, the theory of machine learningâthe foundation of efforts to process that data into knowledgeâhas also advanced. He covers:
Â
âĸÂ The evolution of machine learning
âĸÂ Important learning algorithms and example applications
âĸÂ Using machine learning algorithms for pattern recognition
âĸÂ Artificial neural networks inspired by the human brain
âĸÂ Algorithms that learn associations between instances
âĸÂ Reinforcement learning
âĸÂ Transparency, explainability, and fairness in machine learning
âĸÂ The ethical and legal implicates of data-based decision making
Â
A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programmingâmaking it accessible for everyday readers and easily adoptable for classroom syllabi.
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Ethem AlpaydÃn is Professor in the Department of Computer Engineering at Ãzyegin University and a member of the Science Academy, Istanbul. He is the author of the widely used textbook, Introduction to Machine Learning (MIT Press), now in its fourth edition.
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