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- Title Machine Learning
- Author(s) Abdelhamid Mellouk and Abdennacer Chebira
- Publisher: InTech (January 01, 2009)
- Hardcover/Paperback 450 pages
- eBook PDF Files
- Language: English
- ISBN-10: N/A
- ISBN-13: 978-953-7619-56-1
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Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.About the Authors
- Machine Learning
- Artificial Intelligence
- Data Analysis and Data Mining
- Neural Networks
- Statistics, R Language and SAS Programming
- Operations Research (OR), Linear Programming, Optimization, and Approximation