Machine learning an algorithmic perspective ���ݺ���

����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.