Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

This textbook presents a new approach to numerical analysis for modern computer scientists and introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.

Publication date: 13 Jul 2015

ISBN-10: 1482251884

ISBN-13: 9781482251883

Paperback: 400 pages

Views: 5,549

Type: Textbook

Publisher: A K Peters, Ltd.

License: n/a

Post time: 19 Dec 2016 10:00:00

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics This textbook presents a new approach to numerical analysis for modern computer scientists and introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.
Tag(s): Calculus Computer Vision Linear Algebra Machine Learning Numerical Methods
Publication date: 13 Jul 2015
ISBN-10: 1482251884
ISBN-13: 9781482251883
Paperback: 400 pages
Views: 5,549
Document Type: Textbook
Publisher: A K Peters, Ltd.
License: n/a
Post time: 19 Dec 2016 10:00:00
From the Introduction:
Justin Solomon wrote:Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.

The book covers a wide range of topics -- from numerical linear algebra to optimization and differential equations -- focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material.

The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.

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About The Author(s)


X-Consortium Career Development Assistant Professor and Principal investigator at Geometric Data Processing GroupComputer Science and Artificial Intelligence Laboratory (CSAIL), Department of Electrical Engineering & Computer Science, MIT.

Justin Solomon

X-Consortium Career Development Assistant Professor and Principal investigator at Geometric Data Processing GroupComputer Science and Artificial Intelligence Laboratory (CSAIL), Department of Electrical Engineering & Computer Science, MIT.


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