A First Course in Machine Learning

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 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.