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.

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