Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
Kiadás sorszáma: 1st ed. 2023
Kiadó: Springer
Megjelenés dátuma: 2023. október 4.
Kötetek száma: 1 pieces, Book
Normál ár:
Kiadói listaár:
EUR 69.54
EUR 69.54
Az Ön ára:
26 400 (25 143 Ft + 5% áfa )
Kedvezmény(ek): 8% (kb. 2 296 Ft)
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A termék adatai:
| ISBN13: | 9783031133381 |
| ISBN10: | 3031133382 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 575 oldal |
| Méret: | 235x155 mm |
| Súly: | 1222 g |
| Nyelv: | angol |
| Illusztrációk: | 6 Illustrations, black & white; 156 Illustrations, color |
| 651 |
Témakör:
Rövid leírás:
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.
Hosszú leírás:
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.
Tartalomjegyzék:
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.
