- 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.
- 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.

Here are three eBooks available for free.

MACHINE LEARNING

Edited by Abdelhamid Mellouk and Abdennacer Chebira

Machine Learning can be defined in various ways…

Continue reading “Free Machine Learning eBooks”