Machine Learning,
Edition 2 A Constraint-Based Approach
By Marco Gori, Alessandro Betti and Stefano Melacci

Publication Date: 05 Apr 2023
Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.

The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.

Key Features

  • Presents, in a unified manner, fundamental machine learning concepts, such as neural networks and kernel machines
  • Provides in-depth coverage of unsupervised and semi-supervised learning, with new content in hot growth areas such as deep learning
  • Includes a software simulator for kernel machines and learning from constraints that also covers exercises to facilitate learning
  • Contains hundreds of solved examples and exercises chosen particularly for their progression of difficulty from simple to complex
  • Supported by a free, downloadable companion book designed to facilitate students’ acquisition of experimental skills
About the author
By Marco Gori, Department of Information Engineering and Mathematics, University of Siena, Italy; Alessandro Betti, Postdoctoral Researcher in the Department of Information Engineering and Mathematics (DIISM, University of Siena, Siena, Italy and Stefano Melacci, Senior Researcher (Tenure-Track Assistant Professor), Computer Science, Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
Table of Contents

1 The big picture
2 Learning principles
3 Linear threshold machines
4 Kernel machines
5 Deep architectures
6 Learning with constraints
7 Epilogue
8 Answers to exercises

APPENDIX A Constrained optimization
APPENDIX B Regularization operators
APPENDIX C Calculus of variations
APPENDIX D Index to notation

Book details
ISBN: 9780323898591
Page Count: 560
Retail Price : £79.95
9780128042915; 9780128172162; 9781597492720
Upper level through grad level students taking a machine learning course within computer science, Professionals involved in relevant areas of artificial intelligence