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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

《概率论沉思录(英文版)》不仅适合概率和统计专业人士阅读。也是需要应用统计推断的各领域科技工作者的必读之作。 　　《概率论沉思录(英文版)》是一部奇书。它是著名数学物理学家Jaynes的遗作，凝聚了他对概率论长达40年的深刻思考。原版出版后产生了巨大影响，深受众多专家和学者的好评，并获得Amazon网上书店读者全五星评价。 在书中，作者在H.Jeffreys、R.T.Cox、C.E.Shannon和G.Polya等数学大师思想的基础上继续探索，将概率论置于更大的背景下考察，提出将概率推断作为整个科学的逻辑基础，以适应实际科学研究中对象往往都是信息不完全或者不确定的这一难题，从而超越了传统的概率论，也超越了传统的数理逻辑思维定式。

概率论沉思录--(Probability_Theory_The_Logic_of_Science_)((美)E._T._Jaynes)清晰版PDF

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