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 & 160; 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. & 160; 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: & 160; • The evolution of machine learning •& 160;Important learning algorithms and example applications •& 160;Using machine learning algorithms for pattern recognition •& 160;Artificial neural networks inspired by the human brain •& 160;Algorithms that learn associations between instances •& 160;Reinforcement learning •& 160;Transparency explainability and fairness in machine learning •& 160;The ethical and legal implicates of data-based decision making & 160; 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. Language: English