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Chapters: 1. The ingredients of machine learning.
2. Binary classification and related tasks.
3. Beyond binary classification.
4. Concept learning.
5. Tree models.
6. Rule models.
7. Linear models.
8. Distance-based models.
9. Probabilistic models.
10. Features.
11. Model ensembles.
12. Machine learning experiments.
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Machine Learning

The Art and Science of Algorithms that Make Sense of Data

By Peter Flach - Intelligent Systems Laboratory, University of Bristol, United Kingdom
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Main cover of book
About the book
This book is an introductory text on machine learning. The style of the book is such that it can be used as a textbook for an advanced undergraduate or graduate course, at the same time aiming at interested academics and professionals with a background in neighbouring disciplines. The material includes necessary mathematical detail, but emphasises intuitions and how-to.

The challenge in writing an introductory machine learning text is to do justice to the incredible richness of the machine learning field without losing sight of the unifying principles.

One way in which this is achieved in this book is by separate and extensive treatment of tasks and features, both of which are common across any machine learning approaches. Covering a wide range of logical, geometric and statistical models, the book is one of the most comprehensive machine learning texts around.

For excerpts and lecture slides click
here.
For the Table of Contents, see below.

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Prologue: A machine learning sampler
1. The ingredients of machine learning
2. Binary classification and related tasks
3. Beyond binary classification
4. Concept learning
5. Tree models
6. Rule models
7. Linear models
8. Distance-based models
9. Probabilistic models
10. Features
11. Model ensembles
12. Machine learning experiments

Epilogue: Where to go from here
Important points to remember
Bibliography
Index

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