Kevin P. Murphy Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge), and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley, Postdoc at MIT). In 2004, he became a professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. In 2011, he went to Google in Mountain View, California fo...(展开全部)

Kevin P. Murphy Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge), and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley, Postdoc at MIT). In 2004, he became a professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. In 2011, he went to Google in Mountain View, California fo...(展开全部)

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic a...(展开全部)

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (inc...(展开全部)

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic a...(展开全部)

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (inc...(展开全部)

This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you’ve ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.