Machine Learning Refined: Foundations, Algorithms, and Applications
By Jeremy Watt and Reza Borhani, Aggelos K. Katsaggelos
This text provides a unique approach to machine learning, and contains new and intuitive, but rigorous descriptions, of all the basic concepts needed to conduct research, build products, tamper and play. By prioritizing geometric intuition, computational thinking and real-world practical applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommendation systems, physics and biology, this text provides readers with a clear understanding of the foundational materials as well as practical tools to solve real-world problems.
- Author
- Jeremy Watt and Reza Borhani, Aggelos K. Katsaggelos
- Language
- English
- Size
- 32.4 Mb
- Pages
- 301
- Format
- Year
- 2016
- Edition
- 1
About This Book
This text provides a unique approach to machine learning, and contains new and intuitive, but rigorous descriptions, of all the basic concepts needed to conduct research, build products, tamper and play. By prioritizing geometric intuition, computational thinking and real-world practical applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommendation systems, physics and biology, this text provides readers with a clear understanding of the foundational materials as well as practical tools to solve real-world problems.
Contents
- Chapter 1: Introduction
- Chapter 2: Fundamentals of numerical optimization
- Chapter 3: Regression
- Chapter 4: Classification
- Chapter 5: Automatic feature design for regression
- Chapter 6: Automatic feature design for classification
- Chapter 7: Kernels, backpropagation, and regularized cross-validation
- Chapter 8: Advanced gradient schemes
- Chapter 9: Dimension reduction techniques