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Flach P. Machine Learning: The Art and Science of Algorithms that Make Sense of Data

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Flach P. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394.
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.
Contents:
Preface
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
References
Index
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