Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of the textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
The book, “Understanding Machine Learning: From Theory to Algorithms”, provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
The electronic copy of the book can be downloaded for free, for personal use, here.