Machine Learning Refined
Cambridge University Press (Verlag)
978-1-107-12352-6 (ISBN)
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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.
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.
1. Introduction; 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.
| Erscheinungsdatum | 07.09.2016 |
|---|---|
| Zusatzinfo | Worked examples or Exercises; 3 Tables, black and white; 44 Halftones, color; 91 Line drawings, color |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 179 x 253 mm |
| Gewicht | 740 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 1-107-12352-6 / 1107123526 |
| ISBN-13 | 978-1-107-12352-6 / 9781107123526 |
| Zustand | Neuware |
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