Buch, Englisch, 590 Seiten, Book w. online files / update, Format (B × H): 168 mm x 240 mm, Gewicht: 1005 g
Reihe: Textbook
A Practice-Oriented Approach
Buch, Englisch, 590 Seiten, Book w. online files / update, Format (B × H): 168 mm x 240 mm, Gewicht: 1005 g
Reihe: Textbook
ISBN: 978-3-658-40422-2
Verlag: Springer
This textbook contains the mathematics needed to study computer science in application-oriented computer science courses. The content is based on the author's many years of teaching experience.
The translation of the original German 7 edition Mathematik für Informatiker by Peter Hartmann was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Textbook Features
- You will always find applications to computer science in this book.
- Not only will you learn mathematical methods, you will gain insights into the ways of mathematical thinking to form a foundation for understanding computer science.
- Proofs are given when they help you learn something, not for the sake of proving.
Mathematics is initially a necessary evil for many students. The author explains in each lesson how students can apply what they have learned by giving many real world examples, and by constantly cross-referencing math and computer science. Students will see how math is not only useful, but can be interesting and sometimes fun.
The Content
- Sets, logic, number theory, algebraic structures, cryptography, vector spaces, matrices, linear equations and mappings, eigenvalues, graph theory.
- Sequences and series, continuous functions, differential and integral calculus, differential equations, numerics.
- Probability theory and statistics.
The Target Audiences
Students in all computer science-related coursework, and independent learners.
Zielgruppe
Lower undergraduate
Autoren/Hrsg.
Weitere Infos & Material
DISCRETE MATHEMATICS AND LINEAR ALGEBRA.- Sets and mappings.- Logic.- Natural numbers, complete induction, recursion.- Some number theory.- Algebraic structures.- Vector spaces.- Matrices.- Gaussian algorithm and systems of linear equations.- Eigenvalues, eigenvectors and basis transformations.- Scalar product and orthogonal maps.- Graph theory.- ANALYSIS.- The real numbers.- Sequences and series.- Continuous functions.- Differential calculus.- Integral calculus.- Differential equations.- Numerical methods.- PROBABILITY AND STATISTICS.- Probability spaces.- Random variables.- Important distributions and stochastic processes.- Statistical methods.- Appendix.
