Reviews & Adoption

Used as a reference text at 100+ universities and colleges.

Selected Universities and Colleges

University of Michigan
University of Michigan
University of Toronto
University of Toronto
Texas A&M University
Texas A&M University
Penn State
Penn State
New York University
New York University
Kindai University
Kindai University
Purdue University
Purdue University
Georgia Tech
Georgia Tech
Arizona State University
Arizona State University

Professor Endorsements

John G. Proakis

John G. Proakis

Professor Emeritus, Northeastern University

Highlights the book's path from basic principles to practical implementation.

Osvaldo Simeone

Osvaldo Simeone

Professor, King's College London

Points to the first-principles presentation, geometric intuition, and Python exercises.

David Duvenaud

David Duvenaud

Associate Professor, University of Toronto

Praises the steady build-up of tools, examples, runnable code, and detail.

Kimiaki Shirahama

Kimiaki Shirahama

Professor, Doshisha University

Emphasizes the unified optimization viewpoint and visual explanations.

Reader Review Signals

One of the best books in Machine Learning

Reader on Amazon

“What is an absolute gem are the chapters on Feature Learning, Selections and Engineering.”

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Gold

Estefano Palacios on Amazon

“teaching machine learning rigorously but from first principles”

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Love this book!

Rama Ramakrishnan on Amazon

“The content, the painstakingly created figures, and the beautiful hardback edition are all excellent.”

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One of the best books on the topic

Julio Perez Olvera on Amazon

“Would definitely recommend to anyone starting with ML.”

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Excellent book, great content, improved Kindle edition

Booker on Amazon

“one of the best ML books available”

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Very good book with the right blend of theory and applications

HBK on Amazon

“covers almost all important topics in ML”

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