Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Inbunden, Engelska, 2023

Av Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer

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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.

Produktinformation

Utgivningsdatum2023-10-04
Mått155 x 235 x 38 mm
Vikt1 254 g
FormatInbunden
SpråkEngelska
Antal sidor575
FörlagSpringer International Publishing AG
ISBN9783031133381

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Elektronik och kommunikationer inom Naturvetenskap och teknik
Databaser inom Data och it

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.

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.