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Machine Learning Refined: Foundations, Algorithms, and Applications, by Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

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Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization. �Additional resources including supplemental discussion topics, code demonstrations, and exercises can be found on the official textbook website at mlrefined.com
- Sales Rank: #49976 in Books
- Published on: 2016-11-04
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .75" w x 6.85" l, .0 pounds
- Binding: Hardcover
- 298 pages
About the Author
Jeremy Watt received his PhD in Computer Science and Electrical Engineering from Northwestern University, Illinois. His research interests lie in machine learning and computer vision, as well as numerical optimization.
Reza Borhani received his PhD in Computer Science and Electrical Engineering from Northwestern University, Illinois. His research interests lie in the design and analysis of algorithms for problems in machine learning and computer vision.
Aggelos K. Katsaggelos is a professor and holder of the AT&T chair in the Department of Electrical Engineering and Computer Science at Northwestern University, Illinois, where he also heads the Image and Video Processing Laboratory.
Most helpful customer reviews
3 of 3 people found the following review helpful.
First of its kind - A true textbook that teaches machine learning with a great balance of math and applications
By SeferTech
I am not a machine learning expert. I am not a machine learning novice. But as I am coming to understand a big part of machine learning is really all about curve fitting - and, as an engineer involved with scientific computing, I do know something about that. I have been interested in machine learning for the past year or so and wanting to educate myself to see if it can use it in my job I have been searching for just the right book. Many of the books that are out there seem to be too simply e.g. not enough math. Others are too mathematical and don't explain with words well enough. Others intimately combine ML with programming (usually in Python) to the point whether you learn neither ML or programming. This book "Machine Learning Refined" is different. It seems to be the first book I have found that is rigorous from the beginning (Chapter 2) after a clear introduction and motivation in Chapter 1. The mathematics are not very high level nor are they simplified too much. They are presented at just the right level to be useful. I have breezed through the first few chapters (I usually give up after Chapter 1 or beginning of Chapter 2 in these "new" subjects) and found them crystal clear with great illustrations (even in the Kindle version). Another impressive feature that I have not had time to dive into yet is the extensive end of the chapter exercises. These look like they were assembled with tender loving care to be both interesting and useful to demonstrate the chapter materials. I am also anxious to download and try out the code and other resources available at [...]. The price, even for the Kindle edition, is a bit high but the quality you get over the more popular "tech" titles is clearly worth it. If you have a basic math background and an interest in truly learning ML this is the book for you. I highly recommend it.
3 of 3 people found the following review helpful.
One of the Best Books on ML
By Amazon Customer
I love this book. As the name suggests, all the essential ML concepts are refined in a sub 300 page book that is self contained. The book's clarity without sacrificing mathematical rigor with tons of end of chapter exercises that reinforce concepts make it a unique book. The support site has exercises in python and matlab and additional ipython notebooks for better understanding. The authors are incredibly responsive and supportive. The clarity and exposition condensed in this book is reminiscent of prof Yaser's Learning from Data, but much more practical. I purchased this book after following the author's free chapters online. Totally worth it.
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