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Machine Learning Refined

Machine Learning Refined
Foundations, Algorithms, and Applications

2nd Edition

textbook
  • Publication planned for: December 2019
  • availability: Not yet published - available from December 2019
  • format: Hardback
  • isbn: 9781108480727

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  • 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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    Product details

    • Edition: 2nd Edition
    • Publication planned for: December 2019
    • format: Hardback
    • isbn: 9781108480727
    • dimensions: 247 x 174 mm
    • contains: 316 colour illus. 127 exercises
    • availability: Not yet published - available from December 2019
  • 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.

  • 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 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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