Name: A First Course in Machine Learning By Mark Girolami
SKU: 9781439824146
Price: 6.30 GBP
Availability: InStock

A First Course in Machine Learning Summary

A First Course in Machine Learning by Mark Girolami

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.

A First Course in Machine Learning 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

About Mark Girolami

Simon Rogers is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches a masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, particularly applied to problems in computational biology. His research interests include the analysis of metabolomic data and the application of probabilistic machine learning techniques in the field of human computer interaction. Mark Girolami is a chair of statistics and an honorary professor of computer science at University College London, where he is also the director of the Centre for Computational Statistics and Machine Learning. An EPSRC Advanced Research Fellow, an IET Fellow, and a Fellow of the Royal Society of Edinburgh, Dr. Girolami has made major contributions to the field, including his generalisation of independent component analysis, his work on inference in systems biology, and his innovations in statistical methodology.

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.

Additional information

Sku

GOR007783515

ISBN 13

9781439824146

ISBN 10

1439824142

Title

A First Course in Machine Learning by Mark Girolami

Author

Mark Girolami

Condition

Used - Very Good

Binding type

Hardback

Publisher

Taylor & Francis Inc

Year published

20111025

Number of pages

305

Prizes

N/A

Cover note

Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

Note

This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us

A First Course in Machine Learning Mark Girolami

A First Course in Machine Learning by Mark Girolami

A First Course in Machine Learning Summary

A First Course in Machine Learning by Mark Girolami

A First Course in Machine Learning Reviews

About Mark Girolami

Table of Contents

Additional information

Customer Reviews - A First Course in Machine Learning