Machine Learning
Machine Learning
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MIT presents a concise primer on machine learningcomputer 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 recognitionas well as some we dont 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 learningthe foundation of efforts to process that data into knowledgehas 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 programmingmaking it accessible for everyday readers and easily adoptable for classroom syllabi.
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 recognitionas well as some we dont 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 learningthe foundation of efforts to process that data into knowledgehas 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 programmingmaking it accessible for everyday readers and easily adoptable for classroom syllabi.
