Machine Learning Revised and Updated Edition

<b>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.</b><br> <b> </b><br> <b>No in-depth knowledge of math or programming required!</b> <p/> 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. <p/> 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: <p/> - The evolution of machine learning<br> - Important learning algorithms and example applications<br> - Using machine learning algorithms for pattern recognition<br> - Artificial neural networks inspired by the human brain<br> - Algorithms that learn associations between instances<br> - Reinforcement learning<br> - Transparency explainability and fairness in machine learning<br> - The ethical and legal implicates of data-based decision making <p/> 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.