Machine Learning Refined
Foundations, Algorithms, and Applications
$68.00 ( ) USD
- Jeremy Watt, Northwestern University, Illinois
- Reza Borhani, Northwestern University, Illinois
- Aggelos K. Katsaggelos, Northwestern University, Illinois
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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.Read more
- Provides MATLAB-based coding exercises, real-world examples, and practical applications
- Takes a unique approach, enabling a more coherent, intuitive, and interactive way of learning
- Includes over 150 illustrations, many of which are in full colour
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- Date Published: April 2016
- format: Adobe eBook Reader
- isbn: 9781316560365
- contains: 135 colour illus. 3 tables 81 exercises
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
Part I. The Basics:
2. Fundamentals of numerical optimization
3. Knowledge-driven regression
4. Knowledge-driven classification
Part II. Automatic Feature Design:
5. Automatic feature design for regression
6. Automatic feature design for classification
7. Kernels, backpropagation, and regularized cross-validation
Part III. Tools for Large Scale Data:
8. Advanced gradient schemes
9. Dimension reduction techniques
Part IV. Appendices.
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