PREFACE. I-FUNDAMENTALS OF LEARNING. Introduction to Learning. Learning Theory. Pre-processing and Visualisation. II ESSENTIALS OF TIME SERIES ANALYSIS. Basics of Time Series. Multi-Layer Perceptron (MLP) Neural Networks for Time Series Classification. Dynamic Models for Sequential Data Analysis. III DEEP LEARNING APPROACHES TO TIME SERIES CLASSIFICATION. Clustering for Learning at Deep Level. Deep Time Growing Neural Network. Deep Learning of Cyclic Time Series. Hybrid Method for Cyclic Time Series. Recurrent Neural Networks (RNN). Convolutional Neural Networks. Bibliography.