Machine Learning Revised Updated by Ethem Alpaydin (31 results)

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Paperback. Condition: New. 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.

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Paperback. Condition: New. 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.

Paperback. Condition: new. Paperback. A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.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 makingA 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. "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: New. 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.

Paperback. Condition: new. Paperback. A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.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 makingA 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. "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.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 makingA 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. "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: New. Ethem Alpayd&iacuten is Professor in the Department of Computer Engineering at &Oumlzyegin 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 .