• Title Machine Learning
• Author(s) Abdelhamid Mellouk and Abdennacer Chebira
• Publisher: InTech (January 01, 2009)
• License(s): Attribution 3.0 Unported (CC BY 3.0)
• Hardcover/Paperback 450 pages
• eBook PDF Files
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-953-7619-56-1
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Book Description

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.

• N/A

• Amazon

• 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

• Machine Learning (Abdelhamid Mellouk, et al.)
• PDF Format

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