This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.

Deep learning achieved tremendous results, and it is now common to identify artificial intelligence with deep learning and not with symbol manipulating systems. This results from the paradox of artificial intelligence, a discipline whose principal purpose is its own definition since the terms “intelligence” and “intelligent human behavior” are not very well defined and understood.

This book tells a story outgoing from a perceptron to deep learning highlighted with concrete examples. It discusses some core ideas for the development and implementation of machine learning from three different perspectives: the statistical perspective, the artificial neural network perspective and the deep learning methodology. The book represents a solid foundation in machine learning and should prepare the reader to apply and understand machine learning algorithms as well as to invent new machine learning methods.