内容简介：Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of
About the speaker
Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics. https://www.linkedin.com/in/wiggins/
About the talk
The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks require not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. The speaker will first outline how unsupervised, supervised, and reinforcement learning methods are increasingly used in human applications for description, prediction, and prescription, respectively. The speaker will then focus on the ‘prescriptive’ cases, showing how methods from the reinforcement learning and causal inference literatures can be of direct impact in engineering, business, and decision-making more generally.
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高质量程序设计艺术
斯皮内利斯 / 韩东海 / 人民邮电出版社 / 2008-1 / 55.00元
在本书中，作者回归技术层面。从Apache web server、BSD版本的Unix system、ArgoUMl、ACE网络编程库等著名开源软件中选取了大量真实C、C++和java语言源代码，直观而深刻的阐述了代码中可能存在的各种质量问题，涉及可靠性、安全性、时间性和空间性、可移植性、可维护性以及浮点运算等方面，很多内容都市独辟蹊径，发前人所未发。正因如此，本书继作者的《代码阅读》之后在获Jo......一起来看看 《高质量程序设计艺术》 这本书的介绍吧!
