机器学习的算法观点（第2版 英文版）

　　There have been some interesting developments in machine learning over the past four years，since the lst edition of this book came out. One is the rise of Deep Belief Networks as an area of real research interest（and business interest， as large internet-based companies look to snap up every small company working in the area）， while another is the continuing work on statistical interpretations of machine learning algorithms. This second one is very good for the field as an area of research， but it does mean that computer science students， whose statistical background can be rather lacking， find it hard to get started in an area that they are sure should be of interest to them. The hope is that this book， focussing on the algorithms of machine learrung as it does， will help such students get a handle on the ideas，and that it will start them on a journey towards mastery of the relevant mathematics and statistics as well as the necessary programming and experimentation.In addition， the libraries available for the Python language have continued to develop，so that there are now many more facilities available for the programmer. This has enabled me to provide a simple implementation of the Support Vector Maclune that can be used for experiments， and to simplify the code in a few other places. All of the code that was used to create the examples in the book is available at http：//stephenmonika.net/（in the &Book' tab）， and use and experimentation with any of this code， as part of any study on machine learning， is strongly encouraged.

　　史蒂芬·马斯兰（Stephen Marsland）是新西兰威灵顿维多利亚大学的数学与统计学院教授，他之前在梅西大学任教并担任工程与先进技术学院的研究生教导主任。他是新西兰Te Pūnaha Matatini复杂系统与网络卓越研究中心项目主管，领导复杂性、风险与不确定性等相关主题的研究工作。他是新西兰数学学会的杰出会士，并兼任新西兰数学研究所（NZMRI）所长。