An Introduction to Coding Theory Lecture Notes

Logic Programming by Frank Pfenning

Logic programming is a paradigm where computation arises from proof search in a logic according to a fixed, predictable strategy. It thereby unifies logical specification and implementation in a way that is quite different from functional or imperative programming. This course provides a thorough, modern introduction to logic programming. It consists of a traditional lecture component and a project component. The lecture component introduces the basic concepts and techniques of logic programming followed by successive refinement towards more efficient implementations or extensions to richer logical concepts. We plan to cover a variety of logics and operational interpretations. The project component will be one or several projects related to logic programming. 28-1-2020

Author(s): Frank Pfenning,Carnegie Mellon University

324 Pages

Introduction to Coding Theory Lecture Notes

This note explains the following topics: Linear Codes, Bounds, Asymptotic Bounds and Shannon’s Theorem, Constructing Codes from Other Codes, Generalized Reed-Solomon Codes, Asymptotically Good Codes, Local Decodability, List Decoding, Hard Problems in Coding Theory, The Nearest Codeword Problem and NP-Completeness.

Author(s): Yehuda Lindell

73 Pages

Coding Theory and Applications

This book has been written as lecture notes for students who need a grasp of the basic principles of linear codes. Topics covered includes: Shannon theory and coding, Coding theory, Decoding of linear codes and MacWilliams identity, Coding theory - Constructing New Codes, Coding theory - Bounds on Codes, Reed-Muller codes, Fast decoding of RM codes and higher order RM codes.

Author(s): Enes Pasalic

154 Pages

Course Notes on Coding Theory

This note covers the following topics: Basic codes and constructions, Algebraic Geometry Codes, Limits on Performance of Codes, Algebraic decoding, Algebraic decoding, Linear time decoding, Decoding from random errors and erasures, Applications in complexity theory and Complexity results in coding theory.

Author(s): Madhu Sudan

NA Pages

Essential Coding Theory

This book explains the following topics: Linear Codes, Probability as Fancy Counting and the q-ary Entropy Function, Combinatorics, The Greatest Code of Them All: Reed-Solomon Codes, What Happens When the Noise is Stochastic: Shannon's Theorem, Bridging the Gap Between Shannon and Hamming: List Decoding, Code Constructions, Code Concatenation, Algorithms, Decoding Concatenated Codes, Efficiently Achieving the Capacity of the BSCp, Efficient Decoding of Reed-Solomon Codes, Efficiently Achieving List Decoding Capacity, Applications.

Author(s): Venkatesan Guruswami, Atri Rudra and Madhu Sudan

NA Pages

Structure and Interpretation of Computer Programs

This note covers the following topics: Functions, Values and Side Effects, Control and Higher-Order Functions, Environments and Lambda, Newton's Method and Recursion, Data Abstraction, Sequences and Iterables, Objects, Lists, and Dictionaries, Mutable Data Types, Object-Oriented Programming, Inheritance, Generic Functions, Coercion and Recursive Data, Functional Programming, Declarative Programming, Unification, MapReduce, Parallelism.

Author(s): Amir Kamil

NA Pages

Algorithmic Introduction to Coding Theory

This note introduces the theory of error-correcting codes to computer scientists. This theory, dating back to the works of Shannon and Hamming from the late 40's, overflows with theorems, techniques, and notions of interest to theoretical computer scientists. The course will focus on results of asymptotic or algorithmic significance. Principal topics include: Construction and existence results for error-correcting codes, Limitations on the combinatorial performance of error-correcting codes, Decoding algorithms, Applications in computer science.

Author(s): Madhu Sudan

NA Pages

How To Design Programs An Introduction To Programming and Computing (M. Felleisen, et al)

The main focus of this book is the design process that leads from problem statements to well-organized solutions; it deemphasizes the study of programming language details, algorithmic minutiae, and specific application domains. It covers the following topics: Processing Simple Forms of Data, Processing Arbitrarily Large Data, Abstracting Designs, Generative Recursion, Accumulating Knowledge, Changing the State of Variables, Changing Compound Values.

Author(s): Matthias Felleisen, Robert Bruce Findler, Matthew Flatt and Shriram Krishnamurthi

NA Pages

Programming from the Ground Up (J. Bartlett)

This book covers the following topics: Computer Architecture, Functions, Files, Reading and Writing Simple Records, Developing Robust Programs, Sharing Functions with Code Libraries, Intermediate Memory Topics, High-Level Languages and Optimization.

Author(s): Jonathan Bartlett

326 Pages

Designing and Building Parallel Programs (I. Foster)

This book provides a practitioner's guide for students, programmers, engineers, and scientists who wish to design and build efficient and cost-effective programs for parallel and distributed computer systems. It covers the following topics: Parallel Computers and Computation, Designing Parallel Algorithms, Quantitative Basis for Design, Putting Components Together, Tools, Fortran M, High Performance Fortran, Message Passing Interface and Performance Tools.

Author(s): Ian Foster

NA Pages