Machine Learning Refined: Foundations, Algorithms, and Applications, 2/e (Hardcover)
內容描述
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
目錄大綱
- Introduction to machine learning
Part I. Mathematical Optimization: - Zero order optimization techniques
- First order methods
- Second order optimization techniques
Part II. Linear Learning: - Linear regression
- Linear two-class classification
- Linear multi-class classification
- Linear unsupervised learning
- Feature engineering and selection
Part III. Nonlinear Learning: - Principles of nonlinear feature engineering
- Principles of feature learning
- Kernel methods
- Fully-connected neural networks
- Tree-based learners
Part IV. Appendices: Appendix A. Advanced first and second order optimization methods
Appendix B. Derivatives and automatic differentiation
作者介紹
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).
