The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

Autorentext
Frank Emmert-Streib is Professor of Data Science at Tampere University (Finland). He leads the Predictive Society and Data Analytics Lab, which pursues innovative research in deep learning and natural language processing. The Lab develops and applies high-dimensional methods in machine learning, statistics, and artificial intelligence that can be used to extract knowledge from data in the fields of biology, medicine, social media, social sciences, marketing, or business. Salissou Moutari is Senior Lecturer at Queen's University Belfast (UK) and Interim Director of Research of the Mathematical Science Research Centre (MSRC). His research interests include mathematical modelling, optimization, machine learning and data science, and the applications of these methods to problems from traffic, transportation and distribution systems, production planning and industrial processes. Matthias Dehmer is Professor at UMIT (Austria) and also has a position at Swiss Distance University of Applied Sciences, Brig, Switzerland. His research interests are in complex networks, complexity, data science, machine learning, big data analytics, and information theory. In particular, he is working on machine learning based methods to analyse high-dimensional data.

Inhalt
Introduction.- Introduction to learning from data.- Part 1: General topics.- Prediction models.- Error measures.- Resampling.- Data types.- Part 2: Core methods.- Maximum Likelihood & Bayesian analysis.- Clustering.- Dimension Reduction.- Classification.- Hypothesis testing.- Linear Regression.- Model Selection.- Part 3: Advanced topics.- Regularization.- Deep neural networks.- Multiple hypothesis testing.- Survival analysis.- Generalization error.- Theoretical foundations.- Conclusion. <p