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Fundamentals of Discrete Math for Computer Science
A Problem-Solving Primer
- © 2018
- Latest edition
- Tom Jenkyns 0 ,
- Ben Stephenson 1
Brock University, St. Catharines, Canada
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University of Calgary, Calgary, Canada
- Updated and enhanced new edition with additional material on directed graphs, and on drawing and coloring graphs, as well as more than 100 new exercises (with solutions)
- Highly accessible and easy to read, introducing concepts in discrete mathematics without requiring a university-level background in mathematics
- Ideally structured for classroom-use and self-study, with modular chapters following ACM curriculum recommendations
- Contains examples and exercises throughout the text, and highlights the most important concepts in each section
Part of the book series: Undergraduate Topics in Computer Science (UTICS)
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About this book
This clearly written textbook presents an accessible introduction to discrete mathematics for computer science students, offering the reader an enjoyable and stimulating path to improve their programming competence. The text empowers students to think critically, to be effective problem solvers, to integrate theory and practice, and to recognize the importance of abstraction. Its motivational and interactive style provokes a conversation with the reader through a questioning commentary, and supplies detailed walkthroughs of several algorithms.
This updated and enhanced new edition also includes new material on directed graphs, and on drawing and coloring graphs, in addition to more than 100 new exercises (with solutions to selected exercises).
Students embarking on the start of their studies of computer science will find this book to be an easy-to-understand and fun-to-read primer, ideal for use in a mathematics course taken concurrently with their first programming course.
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Table of contents (11 chapters)
Front matter, algorithms, numbers, and machines.
- Tom Jenkyns, Ben Stephenson
Sets, Sequences, and Counting
Boolean expressions, logic, and proof, searching and sorting, graphs and trees, directed graphs, relations: especially on (integer) sequences, sequences and series, generating sequences and subsets, discrete probability and average-case complexity, turing machines, back matter, authors and affiliations.
Tom Jenkyns
Ben Stephenson
About the authors
Dr. Tom Jenkyns is a retired Associate Professor from the Department of Mathematics and the Department of Computer Science at Brock University, Canada.
Dr. Ben Stephenson is a Teaching Professor in the Department of Computer Science at the University of Calgary, Canada.
Bibliographic Information
Book Title : Fundamentals of Discrete Math for Computer Science
Book Subtitle : A Problem-Solving Primer
Authors : Tom Jenkyns, Ben Stephenson
Series Title : Undergraduate Topics in Computer Science
DOI : https://doi.org/10.1007/978-3-319-70151-6
Publisher : Springer Cham
eBook Packages : Computer Science , Computer Science (R0)
Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
Softcover ISBN : 978-3-319-70150-9 Published: 08 May 2018
eBook ISBN : 978-3-319-70151-6 Published: 03 May 2018
Series ISSN : 1863-7310
Series E-ISSN : 2197-1781
Edition Number : 2
Number of Pages : XIII, 512
Number of Illustrations : 120 b/w illustrations
Topics : Discrete Mathematics in Computer Science , Algorithm Analysis and Problem Complexity
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- Algorithms, Numbers and Machines.- Sets, Sequences and Counting.- Boolean Expressions, Logic and Proof.- Searching and Sorting.- Graphs and Trees.- Relations: Especially on (Integer) Sequences.- Sequences and Series.- Generating Sequences and Subsets.- Discrete Probability and Average Case Complexity.- Turing Machines.
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Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer
About this ebook.
Fundamentals of Discrete Math for Computer Science provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students. The text empowers students to think critically, to be effective problem solvers, to integrate theory and practice, and to recognize the importance of abstraction. Clearly structured and interactive in nature, the book presents detailed walkthroughs of several algorithms, stimulating a conversation with the reader through informal commentary and provocative questions.
Topics and features: highly accessible and easy to read, introducing concepts in discrete mathematics without requiring a university-level background in mathematics; ideally structured for classroom-use and self-study, with modular chapters following ACM curriculum recommendations; describes mathematical processes in an algorithmic manner, often including a walk-through demonstrating how the algorithm performs the desired task as expected; contains examples and exercises throughout the text, and highlights the most important concepts in each section; selects examples that demonstrate a practical use for the concept in question.
This easy-to-understand and fun-to-read textbook is ideal for an introductory discrete mathematics course for computer science students at the beginning of their studies. The book assumes no prior mathematical knowledge, and discusses concepts in programming as needed, allowing it to be used in a mathematics course taken concurrently with a student’s first programming course.
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About the author
Dr. Tom Jenkyns is an Associate Professor in the Department of Mathematics and the Department of Computer Science at Brock University, Canada.
Dr. Ben Stephenson is an Instructor in the Department of Computer Science at the University of Calgary, Canada.
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Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer (Undergraduate Topics in Computer Science)
Description.
Highly accessible and easy to read, introducing concepts in discrete mathematics without requiring a university-level background in mathematics
Ideally structured for classroom-use and self-study, with modular chapters following ACM curriculum recommendations
Contains examples and exercises throughout the text, and highlights the most important concepts in each section
About the Author
Dr. Tom Jenkyns is a retired Associate Professor from the Department of Mathematics and the Department of Computer Science at Brock University, Canada.Dr. Ben Stephenson is a Teaching Professor in the Department of Computer Science at the University of Calgary, Canada.
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About the author (2018).
Dr. Tom Jenkyns is a retired Associate Professor from the Department of Mathematics and the Department of Computer Science at Brock University, Canada.
Dr. Ben Stephenson is a Teaching Professor in the Department of Computer Science at the University of Calgary, Canada.
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Fundamentals of Discrete Math for Computer Science A Problem-Sol
An understanding of discrete mathematics is essential for students of computer science wishing to improve their programming competence.Fundamentals of Discrete Math for Computer Science provides an engaging and motivational introduction to traditional top
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Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one- or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions. For further volumes: http://www.springer.com/series/7592 Tom Jenkyns • Ben Stephenson Fundamentals of Discrete Math for Computer Science A Problem-Solving Primer Tom Jenkyns Department of Mathematics Brock University ON, Canada Ben Stephenson Department of Computer Science University of Calgary AB, Canada ISSN 1863-7310 ISBN 978-1-4471-4068-9 ISBN 978-1-4471-4069-6 (eBook) DOI 10.1007/978-1-4471-4069-6 Springer London Heidelberg New York Dordrecht Library of Congress Control Number: 2012945303 # Springer-Verlag London 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface This book is directed to computer science students at the beginning of their studies. It presents the elements of d
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Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer
Ben stephenson tom jenkyns , ben stephenson.
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First published July 31, 2012
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- How to advance their careers
This is a cohort class for first-year MST students.
This course offers functional skills for & the improvement of organizational processes.
- To manage value creation
- Effective and efficient process design
- Operational design and theory
- To plan and organize functions of management
- To carry out and oversee functions of management
This course teaches scientific reasoning, inquiry, & problem-solving skills.
Topics covered include:
- Simple and theoretical induction
- Bayesianism
- Statistical and causal hypotheses
- Using scientific information in decision making
- Inference to the best explanation
- Science and the individual
- Science and society
Required of all first-year MST students.
This course teaches exploratory data analysis (EDA) & the R coding language.
- Real-world examples
- Scatter plots
- Probability plots
- Residual plots
- The RStudio platform
- Standard and quantitative data evaluation techniques
No prior knowledge of the R coding language needed and you should bring your laptop.
This course assists with methods and skills needed to complete a professional project.
- Realistic goal setting
- Project initiation
- To define objectives and scope
- To identify and manage risk
- Resource allocation
- To keep track of progress
Professional Project
This course bridges the gap between theoretical knowledge and practical skills..
Students will be able to apply diverse skills gained from their studies such as:
- Problem-solving
- Critical thinking
- Communication
- Project management
- Collaboration
This course provides an introduction to the fundamental concepts of probability.
Key topics include:
- Combinatorial problems
- Random variables
- Distributions
- Independence and dependence
- Conditional probability
- Expected value and moments
- Law of large numbers
- Central limit theorems
CS 6150 - 3 Credits - Fall
This course provides an in-depth study of algorithms, focusing on design and analysis.
- Greedy algorithms
- Dynamic programming
- Divide and conquer strategies
- Asymptomatic analysis and recurrence relations
- Graph algorithms and network flows
- Computational complexity and intractability
- NP-hardness and beyond
- Approximation algorithms
3 Credits - Fall
This graduate-level course focuses on the design and implementation of relational database system kernels and other large-scale data management techniques.
- Relational data model and SQL
- File organization, database storage, indexing and hashing
- Query evaluation and optimization
- Transaction processing, concurrency control and recovery
- Database integrity and security
- Latest developments in large-scale data management techniques
Students will participate in a semester-long project to build a mini-database system. Note: This is not a course on building database applications.
Data Mining is about discovering patterns and structures in large data sets. This course will guide you on how to model these problems and find solutions using efficient algorithms. Some of these methods involve the use of randomized algorithms, which are simple to use but can be tricky to analyze. We'll focus on how to use them effectively and provide clear explanations.
This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time.
The curriculum includes a variety of algorithms for both supervised and unsupervised learning. You'll learn about decision trees, online learning, and linear classifiers.
The course also explores:
- Methods to minimize empirical risk
- Computational learning theory
- Ensemble methods
- Bayesian methods
- Techniques for clustering and dimensionality reduction
CS 6630 - 2 Credits - Fall
This course introduces the principles, methods, and techniques for effective visual analysis of data as applied to data science.
We will explore aspects of visualization related to tabular (high-dimensional) data, graphs, text, and maps.
This course also explores:
- Bootstrapping the necessary technical skills (web development with HTML5 and JavaScript)
- Overview of principles from perception and design
- Visualization fundamentals such as interactions and views
- Visualization techniques and methods for non-spatial data types and maps
- Continual analysis, critique, and redesign of visualizations
- Hands-on experience designing and implementing interactive, web-based visualizations using cutting-edge visualization libraries
A complementary course - Visualization for Scientific Data - that focuses on the visualization of spatial data (e.g., grid-based data from simulations and scanning devices) is offered in the spring.
This course is designed to equip students with the mathematical tools necessary for understanding randomness and uncertainty.
The course covers:
- Problem-solving techniques in combinatorics
- Understanding and working with random variables and their distributions
- Concepts of independence and dependence
- Conditional probability and its applications
- Calculation and interpretation of expected values and moments
- Insights into the law of large numbers
- Introduction to central limit theorems
This course is the first part of a comprehensive introduction to statistical inference, focusing on the theoretical foundations of statistics.
- Principles and techniques of sampling
- Understanding sampling distributions
- Application and interpretation of the Central Limit Theorem
- Methods for data transformation
- Concepts of complete and sufficient statistics
- Techniques for point estimation
- Principles of optimality in statistical inference
This course provides an introduction to the fundamental concepts of numerical analysis. It is designed for students with prior programming experience.
- Numerical methods in linear algebra
- Techniques for numerical interpolation
- Numerical integration and differentiation
- Approaches to approximation, including discrete and continuous least squares, Fourier analysis, and wavelets
This course is a continuation of Introduction to Numerical Analysis I. It delves into the numerical solution of initial value problems in ordinary differential equations and introduces students to numerical approaches for solving partial differential equations.
Analysis of Numerical Methods I
This course explores initial- and boundary-value problems of ordinary and partial differential equations.
The course also covers:
- Linear algebra
- Interpolation
- Integration
- Differentiation
- Approximation techniques including least squares, Fourier analysis, and wavelets
This course is a continuation of Analysis of Numerical Methods I.
MATH 5710 - 3 Credits - Fall
This course covers key mathematical concepts and techniques used in various fields, including computer science and data analysis.
The course focuses on:
- Understanding and solving symmetric linear systems
- Working with positive definite matrices
- Solving eigenvalue problems
- Understanding equilibrium equations for both discrete and continuous systems
- Solving boundary value problems in ordinary and partial differential equations
- Applying boundary integrals
This advanced course is designed for students who seek a deeper understanding of statistical theory and its applications.
The course covers a range of topics including:
- Estimation of intervals
- Testing of hypotheses
- Methods based on likelihood
- Understanding of errors and optimality
- Study of order statistics
- Introduction to nonparametric methods
- Exploration of rank statistics
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COMMENTS
This book is directed to computer science students at the beginning of their studies. It presents the elements of discrete mathematics in a form accessible to them and in a way that will improve their programming competence. It focuses on those topics with direct relevance to computer science.
Softcover Book USD 69.99. Price excludes VAT (USA) Compact, lightweight edition. Dispatched in 3 to 5 business days. Free shipping worldwide - see info. This clearly written textbook presents an accessible introduction to discrete mathematics for computer science students.
Title Fundamentals of discrete math for computer science : a problem-solving primer / Tom Jenkyns, Ben Stephenson. Author Jenkyns, T. A. (Tom A.), author. Edition Second edition. ISBN 9783319701516 (electronic book) 3319701517 (electronic book) 9783319701509. 3319701509.
This textbook provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students. The text empowers students to think critically, to be effective problem solvers, to integrate theory and practice, and to recognize the importance of ...
Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer - Ebook written by Tom Jenkyns, Ben Stephenson. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer.
Highly accessible and easy to read, introducing concepts in discrete mathematics without requiring a university-level background in mathematics Ideally structured for classroom-use and self-study, with modular chapters following ACM curriculum recommendations Contains examples and exercises throughout the text, and highlights the most important concepts in each section
Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer, 2nd Edition. May 3, 2018 Books. ... presents an accessible introduction to discrete mathematics for computer science students, offering the reader an enjoyable and stimulating path to improve their programming competence. ... will find this book to be an easy-to ...
An understanding of discrete mathematics is essential for students of computer science wishing to improve their programming competence.Fundamentals of Discrete Math for Computer Science provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students.
Fundamentals of Discrete Math for Computer Science. pp.77-130. Tom Jenkyns. Ben Stephenson. The objective of this chapter is to explain the structure of mathematical proofs, especially proofs of ...
fundamentals-of-discrete-math-for-computer-science-a-problem-solving-primer-undergraduate-topics-in-computer-science 3 Downloaded from resources.caih.jhu.edu on 2020-10-24 by guest 2007-03-15 Koh Khee Meng Graph theory is an area in discrete mathematics which studies configurations (called graphs) involving a set of vertices interconnected by ...
Fundamentals Of Discrete Math For Computer Science A Problem Solving Primer Undergraduate Topics In Computer Science fundamentals-of-discrete-math-for-computer-science-a-problem-solving-primer-undergraduate-topics-in-computer-science 4 Downloaded from nagios.bgc.bard.edu on 2020-06-06 by guest process, every aspect resonates with the
Fundamentals Of Discrete Math For Computer Science A Problem Solving Primer Undergraduate Topics In Computer Science A Matter of Life and Death or Something 2012-02-24 Ben Stephenson Even though he's only ten years old, there are lots of things Arthur Williams knows for sure.
Mathematics for Computer Science. Menu. More Info Online Publication. Syllabus ... MIT6_042JS15_textbook.pdf. Resource Type: Online Textbook. pdf. 5 MB MIT6_042JS15_textbook.pdf Download File DOWNLOAD. Course Info Instructors Prof. Albert R. Meyer; Prof. Adam Chlipala ... assignment Problem Sets. grading Exams. Download Course.
An understanding of discrete mathematics is essential for students of computer science wishing to improve their programming competence.Fundamentals of Discrete Math for Computer Science provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students.
This textbook provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students. The text empowers students to think critically, to be effective problem solvers, to integrate theory and practice, and to recognize the importance of abstraction.
Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or ...
The choices for p are from 0 to k. Once p is chosen, the choices for q are fewer, namely from 0 to k − p. Finally, if p and q are chosen then r is determined, namely r = k − p − q. The number of ways to write k as a sum of three non-negative integers is therefore Xk p=0 kX−i q=0. 1 = Xk p=0. (k −p+1) =.
Back to the content, my assigned textbook on discrete math was, for the portions on counting and relations, unusable. This book is vastly superior. The authors of this book are able to communicate in plain English, organize their work in a sensible manner, and provide clear examples.
Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer. ... textbook presents an accessible introduction to discrete mathematics for computer science students, offering the reader an enjoyable and stimulating path to improve their programming competence. ... science will find this book to be an easy-to-understand and fun ...
Fundamentals Of Discrete Math For Computer Science A Problem Solving Primer Undergraduate Topics In Computer Science 3 3 Solving Primer . 2012. Abstract. This textbook provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students.
Fundamentals Of Discrete Math For Computer Science A Problem Solving Primer Undergraduate Topics In Computer Science 3 3 (Contributor) 2.9 out of 5 stars 10 ratings See all 9 formats and editions Fundamentals of Discrete Math for Computer Science: A ... Buy Fundamentals of Discrete Math for Computer Science: A Problem-Solving Primer (Undergraduate
This textbook provides an engaging and motivational introduction to traditional topics in discrete mathematics, in a manner specifically designed to appeal to computer science students. The text empowers students to think critically, to be effective problem solvers, to integrate theory and practice, and to recognize the importance of abstraction.
In graduate school, 9 credits and up is full-time. Full-time students can complete the program in 2 years. Part-time students take 2.5 to 4 years to complete the program. Example Class Schedules. Transferrable Skills Courses. Data Science. Computational Science. Professional Experience Project.