Results for “Coding theory”
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Abstract Algebra: Theory and Applications
Thomas Judson · University of Puget Sound · 2016
This text is intended for a one- or two-semester undergraduate course in abstract algebra. Traditionally, these courses have covered the theoretical aspects of groups, rings, and fields. However, with the development of computing in the last several decades, applications that involve abstract algebra and discrete mathematics have become increasingly important, and many science, engineering, and computer science students are now electing to minor in mathematics. Though theory still occupies a central role in the subject of abstract algebra and no student should go through such a course without a good notion of what a proof is, the importance of applications such as coding theory and cryptography has grown significantly. Until recently most abstract algebra texts included few if any applications. However, one of the major problems in teaching an abstract algebra course is that for many students it is their first encounter with an environment that requires them to do rigorous proofs. Such students often find it hard to see the use of learning to prove theorems and propositions; applied examples help the instructor provide motivation. This text contains more material than can possibly be covered in a single semester. Certainly there is adequate material for a two-semester course, and perhaps more; however, for a one-semester course it would be quite easy to omit selected chapters and still have a useful text. The order of presentation of topics is standard: groups, then rings, and finally fields. Emphasis can be placed either on theory or on applications. A typical one-semester course might cover groups and rings while briefly touching on field theory, using Chapters 1 through 6, 9, 10, 11, 13 (the first part), 16, 17, 18 (the first part), 20, and 21. Parts of these chapters could be deleted and applications substituted according to the interests of the students and the instructor. A two-semester course emphasizing theory might cover Chapters 1 through 6, 9, 10, 11, 13
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An Open Guide to Data Structures and Algorithms
Paul Bible; Lucas Moser; Mia Scarlato · PALNI · 2023
This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of speed, memory usage, and programming complexity that separate novice programmers from professional practitioners.
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Agent-Based Evolutionary Game Dynamics
Luis Izquierdo; Segismundo Izquierdo; William Sandholm · University of Wisconsin - Madison · 2024
This book is a guide to implement and analyze simple agent-based evolutionary models using NetLogo. All the models we implement are agent-based, i.e. individual agents and their interactions are explicitly represented in the models. To formalise agents’ interactions we use the basic framework of Evolutionary Game Theory. NetLogo is a multi-agent programmable modeling environment used by hundreds of thousands of students, teachers and researchers all around the globe. No coding experience is necessary to fully understand the contents of this book.
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Introduction to Computer Science
Dr. Jean-Claude Franchitti; Amal Alhosban; Mark Buckler; Joanna Gilberti · OpenStax, Rice University
Introduction to Computer Science provides a comprehensive foundation in core computer science concepts and principles, aligning with the scope and sequence of most introductory computer science courses. The offering serves as an engaging entry point for students pursuing diverse fields of study and employment, including computer science, business, engineering, data science, social sciences, and related disciplines. By addressing a broad learner audience—ranging from computer science majors to non-majors—the book offers a thorough introduction to computational thinking and its applications across multiple domains. Introduction to Computer Science is designed to be both interactive and practical, focusing on real-world applications that showcase how core computer science concepts can be used to solve complex problems. Students will explore foundational concepts, such as algorithms, data structures, computer systems organization, and software development, using an array of engaging, hands-on activities. Introduction to Computer Science also includes advanced topics such as machine learning, cybersecurity, and cloud computing, ensuring students have a solid foundation for continued study and future professional success. The offering integrates meaningful learning experiences through chapter-based scenarios, problem-solving exercises, embedded coding activities, and project-based assessments that encourage students to apply what they learn in authentic contexts. The material also includes an array of industry insights and explorations of emerging technology trends in order to provide a holistic approach to learning that extends beyond theory.
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ENG-001A: Composition and Reading
Jill Bonds · Coalinga College
In the contents: 2.4.03: In College Everythings an Argument- A Guide for Decoding College Writing Assignments
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Child Growth & Development Remix 2026
Kim Martin · Copper Mountain College
In the contents: 2.02: Psychosocial Theory · 2.03: Behaviorist Theory · 2.04: Social Cognitive Theory
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General Psychology for Honors Students
Kate Votaw · University of Missouri–St. Louis
In the contents: 1.04: Freud And Psychoanalytic Theory · 20.02: Encoding And Storage- How Our Perceptions Become Memories · 28.03: Erik Erikson's Stage Theory Of Lifespan Development
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Introduction to Psychology (Lumen - Complete)
LibreTexts
In the contents: 4.05: The History of PsychologyPsychoanalytic Theory and Gestalt Psychology · 10.03: Encoding
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Biopsychology
Open Education Resource Initative · OERI
In the contents: 18.09: Chapter 10- The Psychology of Memory Encoding Storage and Retrieval · 18.14: Chapter 14- Mirror Neurons Theory of Mind Social Cognition and Neuroscience of Its Disorders
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Physiological Psychology (IVC PSYCH003)
Ivette Merced · Irvine Valley College
In the contents: 6.20.09: Chapter 10- The Psychology of Memory Encoding Storage and Retrieval · 6.20.14: Chapter 14- Mirror Neurons Theory of Mind Social Cognition and Neuroscience of Its Disorders
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Electrical Engineering
Don Johnson · Rice University
In the contents: 4.06: Encoding Information in the Frequency Domain · 6.21: Source Coding Theorem · 6.23: Subtlies of Coding
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Abstract Algebra: Theory and Applications
Thomas W. Judson · Stephen F. Austing State University
In the contents: 08: Algebraic Coding Theory · 8.05: Efficient Decoding · 23: Galois Theory
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Discrete Structures
Laurie Lacey · SUNY Schenectady County Community College
In the contents: 05: Set Theory · 5.S: Set Theory (Summary) · 08: Topics in Number Theory
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Psychology
Noba · LibreTexts
In the contents: 5.3: Memory (Encoding Storage Retrieval) · 7.4: Theory of Mind
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Friendly Introduction to Mathematical Logic
Christopher Leary & Lars Kristiansen · SUNY Geneseo & University of Oslo
In the contents: 5.05: Coding - Naively · 5.06: Coding Is Representable · 5.12: Coding Deductions
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Biology
John W. Kimball · Tufts University & Harvard University
In the contents: 6.01: One Gene - One Enzyme Theory · 19.1.09: Barcoding
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Quantum Tutorials
Frank Rioux · College of Saint Benedict/Saint John's University
In the contents: 1.51: Quantum Theory Wave-Particle Duality and the Mach-Zehnder Interferometer · 1.95: What Part of the Quantum Theory Dont You Understand · 3.31: Quantum Mechanics Group Theory and C60
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Learning Statistics with R - A tutorial for Psychology Students and other Beginners
DJ Navarro · University of New South Wales
In the contents: 7.02: Transforming and Recoding a Variable · 9.03: Basic Probability Theory
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Introduction to Statistics with R
Edward Chi · Cerritos College
In the contents: 7.03: Basic Probability Theory · 17.02: Transforming and Recoding a Variable
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General Biology
Boundless · LibreTexts
In the contents: 4.03: Studying Cells - Cell Theory · 13.01: Chromosomal Theory and Genetic Linkage · 13.1A: Chromosomal Theory of Inheritance