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Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics, by Justin Solomon
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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.
- Sales Rank: #298842 in eBooks
- Published on: 2015-06-24
- Released on: 2015-06-24
- Format: Kindle eBook
Review
"This book covers an impressive array of topics, many of which are paired with a real-world application. Its conversational style and relatively few theorem-proofs make it well suited for computer science students as well as professionals looking for a refresher."
―Dianne Hansford, FarinHansford.com
About the Author
Justin Solomon�is an NSF Mathematical Sciences Postdoctoral Fellow at Princeton's Program in Applied and Computational Mathematics, where he is studying problems in shape analysis, machine learning, and graphics from a geometric perspective. He received a PhD in computer science from Stanford University, where he was also a lecturer for courses in graphics, differential geometry, and numerical methods. Before his graduate studies, he was a member of Pixar's Tools Research group.
Most helpful customer reviews
3 of 3 people found the following review helpful.
I think it's one of best "advanced introduction" books on numerical methods
By Kudryavtsev Ilya
I have read this book before it was publish because I was a student in Justin's class in Stanford. I think it's one of best "advanced introduction" books on numerical methods. It's clear, it's contemporary, there are a lot of excellent example in graphics, machine learning and other areas, also after each chapter there are interesting exercises from easy ones to open-ending hard problems.
I think the main "plus" of the book that it's gave intuition to reader for many hard problems such as optimization problem, least-squares problems, iterative methods and so on.
Also, in my opinion, linear algebra and it's applications, optimization, non-linear problems are considered in more details than differential equation, giving only intuition and some insights for last topic.
In summary: excellent book on numeric methods for all CS students. I think it's will be very good to read this book before studying machine learning and it's applications and variations.
2 of 2 people found the following review helpful.
Wonderful overview
By Brian Jo
My opinions of this book may be slightly colored by the fact that I was previously a TA for his course. Nonetheless, I think Justin's book is one of the best introductions to "Methods for Computer Vision, Machine Learning, and Graphics" around. It manages to cover a broad range of foundational topics typically covered in their own full-semester courses, such as numerical linear algebra, scientific computing, optimization, and numerical ODEs/PDEs. One of the strengths of the book is that it presents a well-written, holistic overview of these areas with many practical examples and exercises. Another strength is that it uses the language of optimization to frame many of the problems in the later chapters. For those familiar with Stephen Boyd's book on Convex Optimization, I found the overall style to be similar in terms of the balance between theory and practice. I wish I had read this book before all of my PhD coursework--alas, it hadn't been written yet.
Please make sure to consult the errata for the first edition; there were a few typos that came up during the course. I don't believe they detract from the exposition however.
2 of 2 people found the following review helpful.
Justin's book is a fantastic treatment of numerical computing
By nm
Justin's book is a fantastic treatment of numerical computing. Error anaylsis, linear algebra, optimization: this book covers all the fundamentals for anyone interested in computational science and applications. The book is clear, well written, with plenty of examples. Highly recommended for students, teachers, and practitioners.
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