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 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. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

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matlab的欧拉方法代码PDE_Matlab_Code_solution- 作为论文的一部分，我必须用数值​​方法求解PDE。 PDE代表与微生物相关的项目在微观尺度上的梁状结构的位移。 它不是特别适合于解析数学解决方案。 BC取自相关文献，并采用Euler前向有限差​​分法。 PDE读取u_t =（beta / 4）*（s ^ 2 -1） u_ss-（1 / eta） （u_ssss）。

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