Machine Learning: The Art and Science of Algorithms that Make Sense of DataAs 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
A machine learning sampler | 1 |
Probabilistic models | 9 |
The ingredients of machine learning | 13 |
Interaction between features | 44 |
Binary classification and related tasks | 49 |
Beyond binary classification | 81 |
Concept learning | 104 |
2 | 110 |
Probabilistic models | 262 |
Features | 298 |
Categorical ordinal and quantitative features | 304 |
Model ensembles | 330 |
Machine learning experiments | 343 |
Where to go from here | 360 |
Important points to remember | 363 |
| 367 | |
Tree models | 129 |
3 | 138 |
Rule models | 157 |
Linear models | 194 |
Distancebased models | 231 |
| 377 | |
| 383 | |
| 388 | |
| 389 | |
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Machine Learning: The Art and Science of Algorithms that Make Sense of Data Peter Flach No preview available - 2012 |
Common terms and phrases
accuracy basic linear classifier Beak binary classification calculate calibration chapter class distribution clustering concept convex hull covariance coverage curve covered data points data set decision boundary decision rule decision tree defined diagonal discretisation empirical probabilities Equation Euclidean distance exemplars feature tree feature values Figure first-order logic function Gaussian generalise Gills Gills=no Gini index hypothesis space impurity instance space isometrics item sets K-means labelled leaf learning algorithm Length literals logical logistic machine learning margin matrix means measure medoids naive Bayes naive Bayes classifier negative examples normal distribution obtain optimisation overfitting parameters perceptron positive examples predictive probabilistic probability estimates problem ranking regression ROC curve rule list sample scale scores Section segment spam e-mail SpamAssassin split squared subgroup support vector support vector machines target variable task Teeth threshold tion training data training set tree models true univariate unsupervised unsupervised learning variance Viagra words