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First Course in Machine Learning by Mark Girolami and Simon Rogers (2011, Hardcover)
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About this product
Product Identifiers
PublisherCRC Press LLC
ISBN-101439824142
ISBN-139781439824146
eBay Product ID (ePID)92585070
Product Key Features
Number of Pages305 Pages
LanguageEnglish
Publication NameFirst Course in Machine Learning
SubjectGeneral, Databases / Data Mining, Statistics
Publication Year2011
TypeTextbook
Subject AreaComputers, Business & Economics
AuthorMark Girolami, Simon Rogers
FormatHardcover
Dimensions
Item Height0.7 in
Item Weight20.8 Oz
Item Length9.4 in
Item Width6.5 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2011-039389
Reviews"This book offers an introduction to machine learning for students with rather limited background in mathematics and statistics. ... The book is well written and focusses on explaining themain concepts at a very basic level, keeping in mind the limited mathematical background of the intended audience. There are also useful references for further reading at the end of each chapter, and MATLAB code implementing the methods is available online along with the data sets. The code also seems to work well with free alternatives to MATLAB like Octave and FreeMat." --Thoralf Mildenberger, IDP Institute of Data Analysis and Process Design, Zurich University of Applied Sciences, writing in Stat Papers (2015) 56:271 "... the authors do well to keep complicated mathematical notation of the kind sometimes found in statistical texts to a minimum. The concepts are introduced in quite a simple way so as to be intelligible to a reader with no statistical background. ... this introductory text will be useful to computer scientists who need some basic introduction to statistical methods to apply in their respective problems ..." --Arindam Sengupta, International Statistical Review , 2014, "... the authors do well to keep complicated mathematical notation of the kind sometimes found in statistical texts to a minimum. The concepts are introduced in quite a simple way so as to be intelligible to a reader with no statistical background. ... this introductory text will be useful to computer scientists who need some basic introduction to statistical methods to apply in their respective problems ..." --Arindam Sengupta, International Statistical Review , 2014
TitleLeadingA
Dewey Edition22
IllustratedYes
Dewey Decimal006.31
Table Of ContentLinear Modelling: A Least Squares Approach Linear modelling Making predictions Vector/matrix notation Nonlinear response from a linear model Generalisation and over-fitting Regularised least squares Linear Modelling: A Maximum Likelihood Approach Errors as noise Random variables and probability Popular discrete distributions Continuous random variables -- density functions Popular continuous density functions Thinking generatively Likelihood The bias-variance tradeoff Effect of noise on parameter estimates Variability in predictions The Bayesian Approach to Machine Learning A coin game The exact posterior The three scenarios Marginal likelihoods Hyper-parameters Graphical models A Bayesian treatment of the Olympics 100 m data Marginal likelihood for polynomial model order selection Summary Bayesian Inference Nonconjugate models Binary responses A point estimate -- the MAP solution The Laplace approximation Sampling techniques Summary Classification The general problem Probabilistic classifiers Nonprobabilistic classifiers Assessing classification performance Discriminative and generative classifiers Summary Clustering The general problem K-means clustering Mixture models Summary Principal Components Analysis and Latent Variable Models The general problem Principal components analysis (PCA) Latent variable models Variational Bayes A probabilistic model for PCA Missing values Non-real-valued data Summary Glossary Index Exercises and Further Reading appear at the end of each chapter.
SynopsisA First Course in Machine Learningcovers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail., A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http: //bit.ly/firstcourseml), an extensive collection of MATLAB(R)/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
LC Classification NumberQ325.5
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