Sách Understanding Machine Learning From Theory to Algorithms - ACB Bookstore

Giới thiệu Sách Understanding Machine Learning From Theory to Algorithms - ACB Bookstore

Sách Understanding Machine Learning From Theory to Algorithms (sách keo gáy, bìa mềm)

Thể loại:Computers - Artificial Intelligence (AI)

Năm:2014

In lần thứ:draft

Ngôn ngữ:english

Trang:416

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 nonexpert readers in statistics, computer science, mathematics, and engineering.