Machine learning is one of the fastest-growing areas of computer science, with far-reaching applications. This textbook aims 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 previous textbooks have not addressed. 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.