Data Science for Engineers
Reduzierte Artikel in dieser Kategorie
| Verlag | Taylor & Francis |
| Auflage | 2022 |
| Seiten | 360 |
| Format | 15,6 x 2,3 x 23,4 cm |
| Gewicht | 650 g |
| Artikeltyp | Englisches Buch |
| EAN | 9780367754266 |
| Bestell-Nr | 36775426EA |
With tremendous improvement in computational power and availability of rich data, almost all engineering disciplines are using data science in one way or another. This textbook present material on data science comprehensively and in a structured manner.
With tremendous improvement in computational power and availability of rich data, almost all engineering disciplines use data science at some level. This textbook presents material on data science comprehensively, and in a structured manner. It provides conceptual understanding of the fields of data science, machine learning, and artificial intelligence, with enough level of mathematical details necessary for the readers. This will help readers understand major thematic ideas in data science, machine learning and artificial intelligence, and implement first-level data science solutions to practical engineering problems.
The book-
Provides a systematic approach for understanding data science techniques
Explain why machine learning techniques are able to cross-cut several disciplines.
Covers topics including statistics, linear algebra and optimization from a data science perspective.
Provi des multiple examples to explain the underlying ideas in machine learning algorithms
Describes several contemporary machine learning algorithms
The textbook is primarily written for undergraduate and senior undergraduate students in different engineering disciplines including chemical engineering, mechanical engineering, electrical engineering, electronics and communications engineering for courses on data science, machine learning and artificial intelligence.
Inhaltsverzeichnis:
Chapter 1. Introduction to DS, ML AI
Chapter 2. DS and ML - fundamental concepts
Chapter 3. Linear algebra for DS and ML
Chapter 4. Optimization for DS and ML
Chapter 5. Statistical foundations for DS and ML
Chapter 6. Function approximation methods
Chapter 7. Classification methods
Chapter 8. Conclusions and future directions
References
Index
