Get The LION way
Purchase your digital Kindle version or hardcopy at Amazon.com
We believe that education should be for everybody and we decided
to self-publish this book to greatly lower costs (about 8 dollars for
the digital version). But we
are delighted if you value our work as you value a pepperoni pizza by
buying a nicely printed copy.
If you cannot afford the cost, write to us, explain, and we will send you a complimentary copy
(part of the book revenues are given back to the community in this manner).
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.
Download the free PDF sample: The LION way. Machine Learning plus Intelligent Optimization (Battiti - Brunato)
and check if the content and style of our book matches your interests.
In case you are an avid "serial reader", The LION way participates in the
KDP Select and Kindle MatchBook programs (all details
in the Amazon websites above, if you qualify, the digital version is for free).
If you are adopting the book for courses,
some slides and exercises are available at the
LIONcommunity (subscribe if you want to be alerted about new free community materials).
The LION way is currently being translated into Chinese, Spanish and Italian
(estimated completion time: March 2015).
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!",
"I want this book, I'm a coder!",
"friends told me this book is awesome!".
Please cite our book as:
Roberto Battiti, Mauro Brunato
The LION way. Machine Learning plus Intelligent Optimization.
LIONlab, University of Trento, Italy, 2014.
[ BibTeX ]
Chapters 1 and 2
Introduction and nearest neighbors
Learning requires a method
Mastering generalized linear least-squares
Rules, decision trees, and forests
Ranking and selecting features
Specific nonlinear models
Neural networks, shallow and deep
Statistical Learning Theory and Support Vector Machines (SVM)
Democracy in Machine Learning
Top-down clustering: K-means
Bottom-up (agglomerative) clustering
Dimensionality reduction by linear transformations (projections)
Visualizing graphs and networks by nonlinear maps
Automated improvements by local steps
Local Search and Reactive Search Optimization (RSO)
Continuous and Cooperative Reactive Search Optimization (CoRSO)
Multi-Objective Reactive Search Optimization (MORSO)
Text and web mining
Collaborative filtering and recommendation