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Look Inside Machine Learning Refined

Machine Learning Refined
Foundations, Algorithms, and Applications

2nd Edition

$69.99 (X)

  • Date Published: March 2020
  • availability: In stock
  • format: Hardback
  • isbn: 9781108480727
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(2 reviews)

$ 69.99 (X)

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About the Authors
  • With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

    • Encourages geometric intuition and algorithmic thinking to provide an intuitive understanding of key concepts and an interactive way of learning
    • Features coding exercises for Python to help put knowledge into practice
    • Emphasizes practical applications, with real-world examples, to give students the confidence to conduct research, build products, and solve problems
    • Completely self-contained, with appendices covering the essential mathematical prerequisites
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    Reviews & endorsements

    'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego

    'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London

    'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto

    'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan

    'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory

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    Customer reviews

    27th Nov 2020 by UName-989365

    i think this book is very good, i want to buy it

    27th Nov 2020 by UName-1037828

    As a researcher, I found this book a must have for any machine learning researcher.

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    Product details

    • Edition: 2nd Edition
    • Date Published: March 2020
    • format: Hardback
    • isbn: 9781108480727
    • length: 594 pages
    • dimensions: 255 x 183 x 29 mm
    • weight: 1.36kg
    • contains: 316 colour illus. 127 exercises
    • availability: In stock
  • Table of Contents

    1. Introduction to machine learning
    Part I. Mathematical Optimization:
    2. Zero order optimization techniques
    3. First order methods
    4. Second order optimization techniques
    Part II. Linear Learning:
    5. Linear regression
    6. Linear two-class classification
    7. Linear multi-class classification
    8. Linear unsupervised learning
    9. Feature engineering and selection
    Part III. Nonlinear Learning:
    10. Principles of nonlinear feature engineering
    11. Principles of feature learning
    12. Kernel methods
    13. Fully-connected neural networks
    14. Tree-based learners
    Part IV. Appendices: Appendix A. Advanced first and second order optimization methods
    Appendix B. Derivatives and automatic differentiation
    Appendix C. Linear algebra.

  • Resources for

    Machine Learning Refined

    Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

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  • Authors

    Jeremy Watt, Northwestern University, Illinois
    Jeremy Watt received his Ph.D. in Electrical Engineering from Northwestern University, Illinois, and is now a machine learning consultant and educator. He teaches machine learning, deep learning, mathematical optimization, and reinforcement learning at Northwestern University, Illinois.

    Reza Borhani, Northwestern University, Illinois
    Reza Borhani received his Ph.D. in Electrical Engineering from Northwestern University, Illinois, and is now a machine learning consultant and educator. He teaches a variety of courses in machine learning and deep learning at Northwestern University, Illinois.

    Aggelos K. Katsaggelos, Northwestern University, Illinois
    Aggelos K. Katsaggelos is the Joseph Cummings Professor at Northwestern University, Illinois, where he heads the Image and Video Processing Laboratory. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), SPIE, the European Association for Signal Processing (EURASIP), and The Optical Society (OSA) and the recipient of the IEEE Third Millennium Medal (2000).

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