The LION way

Machine Learning plus Intelligent Optimization
Roberto Battiti and Mauro Brunato
LIONlab, University of Trento, Italy, Feb 2014.

 

This freshly printed book presents two topics which are in most cases separated: machine learning (the design of flexible models from data) and intelligent optimization (the automated creation and selection of improving solutions). Both topics are considered technical and we do not expect our book to be for the masses. But for sure, more and more innovative and bold people (lionhearted?) can now master the source of power arising from LION techniques to solve problems, improve businesses, create new applications.
Powerful tools are not only for cognoscenti and this book does a serious effort to distinguish the paradigm shift brought about by machine learning and intelligent optimization methods from the fine details, and it does not refrain from presenting concrete examples and vivid images.

To check if you like the content and the style of the book, download: sample chapters 1-6, and table of contents (6 MB).

The opinions of our readers give additional hints about the book style. A wonderfully warm introduction to Machine Learning, Informative and well written, Couldn't stop reading, Smooth read, covers the essential ideas, will help even experienced practitioners avoid mistakes. .

The messages we receive give us a boost to wake up on cloudy days, and we try to answer in person (but be patient, some days we are very loaded). Some excerpts from our emails: "what an amazing book", "thank you very much for your sharing!", "Very good book! Well done man!", "Thanks for this nice book!!!It's very interesting and useful!", "very entertaining!", "I want this book, I'm a coder!", "friends told me this book is awesome!".

If you are adopting the book for courses, some slides and exercises are available at the LIONcommunity.

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LIONbook

Please cite as:
Roberto Battiti, Mauro Brunato
The LION way. Machine Learning plus Intelligent Optimization.
LIONlab, University of Trento, Italy, 2014.
ISBN: 978-14-960340-2-1
[ BibTeX ]

The LION Way is available for purchase.

Get your complimentary copy

A personal copy is available on demand for non-profit use.

Why for free? Because the authors believe that education and culture should be for everybody. We hope to see the material used by many students, instructors and practitioners everywhere and to receive a lot of constructive feedback to improve the book in the next years. You may also be interested in contributing to our LIONcommunity . However, freely accessible doesn't mean that everyone has the right to copy and spread the material, the free copy is strictly personal and for non-profit usages (and water-marked).
We decided to self-publish this book to greatly lower costs (as low as about 9 dollars for the Kindle version) and we are delighted when our readers demonstrate that they value our work as they value a pepperoni pizza by buying a nicely printed copy or a color e-book version (see above).

Please be patient, some days are very hectic and we (the authors) personally handle your requests. Write your email address below and wait for an email by our LIONlab mailing list with a link to your personal copy to download (also, you may want to check your anti-spam filter to authorize our messages if they end up there).

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Chapters

Chapters 1 and 2

Page1

Chapters 1 and 2

Introduction and nearest neighbors

Chapter 3

Page1

Chapter 3

Learning requires a method

Chapter 4

Page1

Chapter 4

Linear models

Chapter 5

Page1

Chapter 5

Mastering generalized linear least-squares

Chapter 6

Page1

Chapter 6

Rules, decision trees, and forests

Chapter 7

Page1

Chapter 7

Ranking and selecting features

Chapter 8

Page1

Chapter 8

Specific nonlinear models

Chapter 9

Page1

Chapter 9

Neural networks, shallow and deep

Chapter 10

Page1

Chapter 10

Statistical Learning Theory and Support Vector Machines (SVM)

Chapter 11

Page1

Chapter 11

Democracy in Machine Learning

Chapter 12

Page2

Chapter 12

Top-down clustering: K-means

Chapter 13

Page2

Chapter 13

Bottom-up (agglomerative) clustering

Chapter 14

Page2

Chapter 14

Self-organizing maps

Chapter 15

Page2

Chapter 15

Dimensionality reduction by linear transformations (projections)

Chapter 16

Page2

Chapter 16

Visualizing graphs and networks by nonlinear maps

Chapter 17

Page2

Chapter 17

Semi-supervised learning

Chapter 18

Page2

Chapter 18

Automated improvements by local steps

Chapter 19

Page2

Chapter 19

Local Search and Reactive Search Optimization (RSO)

Chapter 20

Page2

Chapter 20

Continuous and Cooperative Reactive Search Optimization (CoRSO)

Chapter 21

Page2

Chapter 21

Multi-Objective Reactive Search Optimization(MORSO)

Chapter 22

Page2

Chapter 22

Text and web mining

Chapter 23

Page2

Chapter 23

Collaborative filtering and recommendation
Web

Chapter 5

Mastering generalized linear least-squares

Web

Chapter 6

Rules, decision trees, and forests

Web

Chapter 7

Ranking and selecting features

Web

Chapter 8

Specific nonlinear models

Web

Chapter 9

Neural networks, shallow and deep

Web

Chapter 10

Statistical Learning Theory and Support Vector Machines (SVM)

Web

Chapter 11

Democracy in Machine Learning

Web

Chapter 12

Top-down clustering: K-means

Web

Chapter 13

Bottom-up (agglomerative) clustering

Web

Chapter 14

Self-organizing maps

Web

Chapter 15

Dimensionality reduction by linear transformations (projections)

Web

Chapter 16

Visualizing graphs and networks by nonlinear maps

Web

Chapter 17

Semi-supervised learning

Web

Chapter 18

Automated improvements by local steps

Web

Chapter 19

Local Search and Reactive Search Optimization (RSO)

Web

Chapter 20

Continuous and Cooperative Reactive Search Optimization (CoRSO)

Web

Chapter 21

Multi-Objective Reactive Search Optimization (MORSO)

Web

Chapter 22

Text and web mining

Web

Chapter 23

Collaborative filtering and recommendation