Machine Learning : A Constraint-Based Approach (Second edition)

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 includes 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. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. Special attention is reserved 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, and includes 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.

This new edition is accompanied by a free downloadable companion book. The companion book focuses on providing concrete examples with in-depth discussions on coding and experiments. The reader is expected to use the companion book as a fast gateway to the discipline. At the same time, extensive referencing to the main textbook will stimulate and encourage the acquisition of foundational and mathematical details, along with algorithmic issues. The simple application-based problems covered in the book are solved by using multiple Python implementations of different Machine Learning models.