Machine learning an algorithmic perspective

Machine learning an algorithmic perspective ÄÚÈݼò½é

¡¡¡¡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£º//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.