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.

• 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