Building Blocks for Theoretical Computer Science (Version 1.3)
This book teaches you how to read and write mathematical proofs. It also gives a brief introduction to some key topics in theoretical computer science: algorithm analysis and complexity, automata theory, and computability.
Tag(s): Proofs Theory of Computation
Publication date: 01 Jan 2013
ISBN-10: n/a
ISBN-13: n/a
Paperback: 271 pages
Views: 8,976
Type: N/A
Publisher: n/a
License: n/a
Post time: 11 May 2016 12:00:00
Building Blocks for Theoretical Computer Science (Version 1.3)
This book teaches you how to read and write mathematical proofs. It also gives a brief introduction to some key topics in theoretical computer science: algorithm analysis and complexity, automata theory, and computability.
Publication date: 01 Jan 2013
ISBN-10: n/a
ISBN-13: n/a
Paperback: 271 pages
Views: 8,976
Document Type: N/A
Publisher: n/a
License: n/a
Post time: 11 May 2016 12:00:00
Margaret M. Fleck wrote:This book teaches two different sorts of things, woven together. It teaches you how to read and write mathematical proofs. It provides a survey of basic mathematical objects, notation, and techniques which will be useful in later computer science courses. These include propositional and predicate logic, sets, functions, relations, modular arithmetic, counting, graphs, and trees. And, finally, it gives a brief introduction to some key topics in theoretical computer science: algorithm analysis and complexity, automata theory, and computability.
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About The Author(s)
Margaret M. Fleck is a Research Associate Professor in the Department of Computer Science, University of Illinois, Urbana-Champaign. Her research interests include computational linguistics, computer vision, and programming language tools to support language and vision research. Right now, she's working on unsupervised algorithms that learn word boundaries from transcribed speech.
Margaret M. Fleck is a Research Associate Professor in the Department of Computer Science, University of Illinois, Urbana-Champaign. Her research interests include computational linguistics, computer vision, and programming language tools to support language and vision research. Right now, she's working on unsupervised algorithms that learn word boundaries from transcribed speech.