This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decisio...(展开全部)

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 t...(展开全部)

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 t...(展开全部)

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.

这本热情洋溢的信息理论基础入门书从经典香农理论到统计学习中的现代应用，为学生进一步学习打下了独特、全面而严谨的基础。本书使用独特的有限块长度方法介绍了数据压缩、信道编码和速率失真理论等核心主题。本书通过超过 210 个课后练习和大量示例，向学生介绍了统计学、机器学习和现代通信理论的当代应用。本书介绍了信息理论方法在统计学习和计算机科学中的应用，例如 f 散度、PAC Bayes 和变分原理、Kolmogorov 度量熵、强数据处理不等式和统计估计的熵上界。本书附有教师解决方案手册和关于信息理论中更专业主题的额外独立章节，是电气工程、统计学和计算机科学专业高年级本科生和研究生的理想入门教科书。 ·系统地处理统计学习和高维统计中的信息论技术 ·为连续和离散变量开发信息论，提供与统计和机器学习应用相关的示例 ·重点关注有限块长度（非渐近）结果，让学生掌握 ...(展开全部)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and ex...(展开全部)

If you’re training a machine learning model but aren’t sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model’s lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning impleme...(展开全部)

It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the co...(展开全部)