Matthias Plaue

Data Science

An Introduction to Statistics and Machine Learning

• Broschiertes Buch

Produktbeschreibung

This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

Produktdetails

• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-662-67881-7
• 1st ed. 2023
• Seitenzahl: 388
• Erscheinungstermin: 1. September 2023
• Englisch
• Abmessung: 235mm x 155mm x 21mm
• Gewicht: 587g
• ISBN-13: 9783662678817
• ISBN-10: 3662678810
• Artikelnr.: 68360538

Autorenporträt

Matthias Plaue is a versatile researcher with a background in mathematical physics. He has explored diverse domains, spanning from relativity theory to pedestrian dynamics. As a data scientist, he develops algorithms for data analysis and artificial intelligence, tailored to support strategic decision-making. In addition to his professional pursuits, he has devoted considerable time to mentoring students, imparting a deep understanding of mathematics and its practical application in tackling complex problems across the fields of science, technology, and engineering.

Inhaltsangabe

Preface.- Part I Basics.- 1 Elements of data organization.- 2 Descriptive statistics.- Part II Stochastics.- 3 Probability theory.- 4 Inferential statistics.- 5 Multivariate statistics.- Part III Machine learning.- 6 Supervised machine learning.- 7 Unsupervised machine learning.- 8 Applications of machine learning.- Appendix.- A Exercises with answers.- B Mathematical preliminaries.- Supplementary literature.- Index.

Rezensionen

"The book covers a wide range of topics, from basic statistical concepts to advanced machine learning algorithms. It is both deep and broad, making it a valuable resource for both beginners and experienced practitioners. Each concept is well explained and often accompanied by practical examples, which enhances understanding. The inclusion of real-world examples and applications of machine learning techniques is a major strength." (Wael Badawy, Computing Reviews, March 1, 2024)