The recent financial crisis was an accident, a "perfect storm" fueled by an unforeseeable confluence of events that unfortunately combined to bring down the global financial systems. And policy makers? They did everything they could, given their limited authority. It was all a terrible, unavoidable accident. Or at least this is the story told and retold by a chorus of luminarie...(展开全部)

The recent financial crisis was an accident, a "perfect storm" fueled by an unforeseeable confluence of events that unfortunately combined to bring down the global financial systems. And policy makers? They did everything they could, given their limited authority. It was all a terrible, unavoidable accident. Or at least this is the story told and retold by a chorus of luminarie...(展开全部)

克里斯汀•麦金莱：美国畅销作家，音乐人，著名主持人，同时还是一名机械工程师，曾参与电力生产、工业技术以及商业建造等重大工程。

克里斯汀•麦金莱：美国畅销作家，音乐人，著名主持人，同时还是一名机械工程师，曾参与电力生产、工业技术以及商业建造等重大工程。

Financial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.

严玉星，毕业于麦吉尔大学，获金融学博士学位。他有着丰富的教学经验，教授过各类本科学位和研究生学位的金融课程，如金融建模、期权和期货、投资组合理论、定量财务分析、企业融资和金融数据库等。他曾在8所全球知名的大学任教：两所在加拿大，一所在新加坡，5所在美国。 严博士一直活跃于学术研究的前沿，他的研究成果在多个国际学术期刊发表。此外，他还是财务数据方面的专家。在新加坡南洋理工大学任教时，他曾为博士生讲授一门名为“金融数据库入门”的课程。

严玉星，毕业于麦吉尔大学，获金融学博士学位。他有着丰富的教学经验，教授过各类本科学位和研究生学位的金融课程，如金融建模、期权和期货、投资组合理论、定量财务分析、企业融资和金融数据库等。他曾在8所全球知名的大学任教：两所在加拿大，一所在新加坡，5所在美国。 严博士一直活跃于学术研究的前沿，他的研究成果在多个国际学术期刊发表。此外，他还是财务数据方面的专家。在新加坡南洋理工大学任教时，他曾为博士生讲授一门名为“金融数据库入门”的课程。

凯瑟琳•拉丝基 美国著名作家，已出版“绝境狼王”系列等70多部作品，曾荣获纽伯瑞儿童文学奖银奖、美国全国犹太图书奖、美国图书馆协会颁发的ALA最佳童书奖。她还是《波士顿环球报》奖、《华盛顿邮报》儿童图书协会奖得主。 从开始写作至今，狼、猫头鹰、熊等动物一直是凯瑟琳•拉丝基爱好、研究的对象。借丈夫任职美国《国家地理》摄影记者、纪录片制片人之便，凯瑟琳•拉丝基有机会深入动物的生存环境，观察动物习性，了解它们的生存习性与生活细节。真实细致的习性描摹、瑰丽多彩的文学想象与优雅传神的文学笔墨，使她赢得美国“动物奇幻小说女王”的美誉。

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.