Product details:
ISBN13: | 9783031133381 |
ISBN10: | 3031133382 |
Binding: | Hardback |
No. of pages: | 575 pages |
Size: | 235x155 mm |
Weight: | 1222 g |
Language: | English |
Illustrations: | 6 Illustrations, black & white; 156 Illustrations, color |
642 |
Category:
Engineering in general
Electrical engineering and telecommunications, precision engineering
System analysis, system planning
Operating systems and graphical user interfaces
Artificial Intelligence
Media and information industry
Engineering in general (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
System analysis, system planning (charity campaign)
Operating systems and graphical user interfaces (charity campaign)
Artificial Intelligence (charity campaign)
Media and information industry (charity campaign)
Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
Edition number: 1st ed. 2023
Publisher: Springer
Date of Publication: 4 October 2023
Number of Volumes: 1 pieces, Book
Normal price:
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EUR 69.54
EUR 69.54
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22 956 (21 863 HUF + 5% VAT )
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Discount is valid until: 30 June 2024
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Short description:
In recent years, large amounts of data became available in all areas of science, industry and society. This provides unprecedented opportunities for enhancing our knowledge, and to solve scientific and societal problems. In order to emphasize the importance of this, data have been called the "oil of the 21st Century". Unfortunately, data do usually not reveal information easily, but analysis methods are required to extract it. This is the main task of data science.
The textbook provides students with tools they need to analyze complex data using methods from machine learning, artificial intelligence and statistics. These are the main fields comprised by data science. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. This allows the immediate practical application of the learning concepts side-by-side.
The book advocates an integration of statistical thinking, computational thinking and mathematical thinking because data science is an interdisciplinary field requiring an understanding of statistics, computer science and mathematics. Furthermore, the book highlights the understanding of the domain knowledge about experiments or processes that generate or produce the data. The goal of the authors is to provide students with a systematic approach to data science that allows a continuation of the learning process beyond the presented topics. Hence, the book enables learning to learn.
Main features of the book:- emphasizing the understanding of methods and underlying concepts- integrating statistical thinking, computational thinking and mathematical thinking- highlighting the understanding of the data- exploring the power of visualizations- balancing theoretical and practicalpresentations - demonstrating the application of methods using R- providing detailed examples and discussions- presenting data science as a complex network
Elements of Data Science, Machine Learning and Artificial Intelligence using R presents basic, intermediate and advanced methods for learning from data, culminating into a practical toolbox for a modern data scientist. The comprehensive coverage allows a wide range of usages of the textbook from (advanced) undergraduate to graduate courses.
Long description:
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.
Table of Contents:
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.