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A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition) [Hardcover]

Simon Rogers , Mark Girolami
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Table of Contents

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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