Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

Machine Learning Refined

• Gebundenes Buch

Produktbeschreibung

An intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lecture slides, and solutions. It is ideal for graduate courses, reference, and self-study.

Produktdetails

• Verlag: Cambridge University Press
• 2., überarb. Aufl.
• Seitenzahl: 598
• Erscheinungstermin: 9. Januar 2020
• Englisch
• Abmessung: 250mm x 175mm x 36mm
• Gewicht: 1351g
• ISBN-13: 9781108480727
• ISBN-10: 1108480721
• Artikelnr.: 57015964

Autorenporträt

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.

Inhaltsangabe

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.

Rezensionen

'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