Machine learning : a journey to deep learning : with exercises and answers Preface Introduction Probability and information Linear algebra and optimization Linear and nonlinear regression Perceptron Multilayer perceptron Learning theory Model selection Clustering Radial basis networks Support vector machines Deep learning Convolutional networks Recurrent networks Autoencoders Epilogue Bibliography Index. 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 Publishers website