Flach P. Machine Learning. The Art and Science of Algorithms...2012

Textbook in PDF format

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
Prologue: A machine learning sampler.
The ingredients of machine learning.
Tasks: the problems that can be solved with machine learning.
Models: the output of machine learning.
Features: the workhorses of machine learning.
Binary classification and related tasks.
Classification.
Scoring and ranking.
Class probability estimation.
Beyond binary classification.
Handling more than two classes.
Regression.
Unsupervised and descriptive learning.
Concept learning.
The hypothesis space.
Paths through the hypothesis space.
Beyond conjunctive concepts.
Learnability.
Tree models.
Decision trees.
Ranking and probability estimation trees.
Tree learning as variance reduction.
Rule models.
Learning ordered rule lists.
Learning unordered rule sets.
Descriptive rule learning.
First-order rule learning.
Linear models.
The least-squares method.
The perceptron.
Support vector machines.
Obtaining probabilities from linear classifiers.
Going beyond linearity with kernel methods.
Distance-based models.
So many roads...
Neighbours and exemplars.
Nearest-neighbour classification.
Distance-based clustering.
Hierarchical clustering.
From kernels to distances.
Probabilistic models.
The normal distribution and its geometric interpretations.
Probabilistic models for categorical data.
Discriminative learning by optimising conditional likelihood.
Probabilistic models with hidden variables.
Compression-based models.
Features.
Kinds of feature.
Feature transformations.
Feature construction and selection.
Model ensembles.
Bagging and random forests.
Boosting.
Mapping the ensemble landscape.
Machine learning experiments.
What to measure.
How to measure it.
How to interpret it.
Epilogue: Where to go from here.
Important points to remember