Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)

A concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars.<BR><BR>No in-depth knowledge of math or programming required!<BR><BR>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.<BR><BR>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:<BR><BR>• 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<BR><BR>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.