A first course in machine learning
著者
書誌事項
A first course in machine learning
（Chapman & Hall/CRC machine learning & pattern recognition series）（A Chapman & Hall book）
CRC Press/Taylor & Francis, c2012
- : hardback
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Linear 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.
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