Machine Learning: An Algorithmic Perspective,Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
by: Stephen Marsland
ISBN-10: 1466583282
ISBN-13: 9781466583283
Edition 版次: 2
Publication Date 出版日期: 2014-10-08
Print Length 页数: 457
Book Description
By finelybook
Review
“I thought the first edition was hands down,one of the best texts covering applied machine learning from a Python perspective. I still consider this to be the case. The text,already extremely broad in scope,has been expanded to cover some very relevant modern topics … I highly recommend this text to anyone who wants to learn machine learning … I particularly recommend it to those students who have followed along from more of a statistical learning perspective (Ng,Hastie,Tibshirani) and are looking to broaden their knowledge of applications. The updated text is very timely,covering topics that are very popular right now and have little coverage in existing texts in this area.”
―Intelligent Trading Tech blog,April 2015
“The book’s emphasis on algorithms distinguishes it from other books on machine learning (ML). This is further highlighted by the extensive use of Python code to implement the algorithms. … The topics chosen do reflect the current research areas in ML,and the book can be recommended to those wishing to gain an understanding of the current state of the field.”
―J. P. E. Hodgson,Computing Reviews,March 27,2015
“I have been using this textbook for an undergraduate machine learning class for several years. Some of the best features of this book are the inclusion of Python code in the text (not just on a website),explanation of what the code does,and,in some cases,partial numerical run-throughs of the code. This helps students understand the algorithms better than high-level descriptions and equations alone and eliminates many sources of ambiguity and misunderstanding.”
―Daniel Kifer
“This book will equip and engage students with its well-organised and -presented material. In each chapter,they will find thorough explanations,figures illustrating the discussed concepts and techniques,lots of programming (Python) and worked examples,practice questions,further readings,and a support website. The book will also be useful to professionals who can quickly inform and refresh their memory and knowledge of how machine learning works and what are the fundamental approaches and methods used in this area. As a whole,it provides an essential source for machine learning methodologies and techniques,how they work,and what are their application areas.”
―Ivan Jordanov,University of Portsmouth,UK
Praise for the First Edition 版次:
“… liberally illustrated with many programming examples,using Python. It includes a basic primer on Python and has an accompanying website. It has excellent breadth and is comprehensive in terms of the topics it covers,both in terms of methods and in terms of concepts and theory. … I think the author has succeeded in his aim: the book provides an accessible introduction to machine learning. It would be excellent as a first exposure to the subject,and would put the various ideas in context …”
―David J. Hand,International Statistical Review (2010),78
“If you are interested in learning enough AI to understand the sort of new techniques being introduced into Web 2 applications,then this is a good place to start. … it covers the subject matter of many an introductory course on AI and it has references to the source material and further reading but it is written in a fairly casual style. Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on … . This is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.”
―I-Programmer,November 2009
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About the Author
Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces,Euler equations,machine learning,and algorithms. He received a PhD from Manchester University
Contents
Introduction
Preliminaries
Neurons,NNs & Linear Discriminants
Multi-Layer Perceptron
Radial Basis Functions & Splines
Dimensionality Reduction
Probabilistic Learning
Support Vector Machines
Optimisation & Search
Evolutionary Learning
Reinforcement Learning
Learning with Trees
Decision by Committee-Ensemble Learning
Unsupervised Learning
Markov Chain Monte Carlo(MCMC)
Graphical Models
Symmetric Weights & Deep Belief Networks
Gaussian Processes
Python
