Understanding Machine Learning

From Theory to Algorithms

Paper Book

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.

Conditions of Use

CC BY-NC-SAThis book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Understanding Machine Learning for free.

Title
Understanding Machine Learning
Subtitle
From Theory to Algorithms
Publisher
Author(s)
,
Published
2014-07-17
Edition
1
Format
eBook (pdf, epub, mobi)
Pages
410
Language
English
ISBN-10
1107057132
ISBN-13
9781107057135
License
CC BY-NC-SA
Book Homepage
Free eBook, Errata, Code, Solutions, etc.
Related Books