Linear matrix inequalities in system and control theory

Daniel A. Spielman Dept. of Computer Science Program in Applied Mathematics Yale Unviersity

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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 non-expert readers in statistics, computer science, mathematics, and engineering.

Daniel Liberzon 的关于变分法和最优控制的英文书，Calculus of Variations and Optimal Control Theory，适合入门和打基础。

A course in game theory 中文+英文 高清带书签，博弈论入门经典书籍

非扫描、高清版本 UNDERSTANDING MACHINE LEARNING From Theory to Algorithms Shai Shalev-Shwartz The Hebrew University, Jerusalem Shai Ben-David University of Waterloo, Canada

自动机理论、语言和计算导论（第三版）英文原版答案 Solutions to Introduction to Automata Theory, Languages, and Computation

Solutions for Introduction to Automata Theory, Languages, and Computation