This is an introductory book on Pattern Recognition, including Machine Learning methods, which is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduateor advanced undergraduate level. It can also be used for self-study or as a supplemental text for courses in Machine Learning, Bioinformatics, and Materials Informatics. The purpose of the book is to be concise but thorough: the text avoids being verbose; it is selective, meaning that it is not an encyclopedic approach as seen in some other textbooks. Despite this, it covers all topics commonly used in prediction from data, including classification, feature selection, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks.  weiterlesen