This note
covers the following topics: Introduction to programming, Use of objects and
variables, Definition of methods and classes, Primitive data types, Conditional
statements, Loop statements, Arrays and matrices, Files and input/output
streams, Program errors and exception handling, Recursion, Dynamic arrays and
linked lists.
This note exlains the following toipics: Basic Business Data Analysis, Python as a Basic and Business Calculator,
X Y Plots, Simple Data Analysis, Manipulating Data and More Complex Data
Analysis, Reading In and Writing Out Text Data, Automating and Managing
Information Systems, Managing Files, Managing Collections of Files Directories,
Managing Collections of Files Searching and Designing Power
Programs.
This note covers the following
topics: Sphere Packing and Shannon’s Theorem, Linear Codes, Hamming Codes,
Generalized Reed-Solomon Codes, Modifying Codes, Codes over Subfields, Cyclic
Codes, Weight and Distance Enumeration.
Coding theory includes the study of compression codes which enable us
to send messages cheaply and error correcting codes which ensure that messages
remain legible even in the presence of errors. Topics covered includes: Codes
and alphabets, Huffman’s algorithm, Shannon’s noiseless coding theorem , Hamming’s breakthrough, Shannon’s noisy coding theorem , Linear codes,
Polynomials and fields , Cyclic codes, Stream ciphers, Asymmetric systems,
Commutative public key systems, Trapdoors and signatures.
This note
covers the following topics: Introduction to programming, Use of objects and
variables, Definition of methods and classes, Primitive data types, Conditional
statements, Loop statements, Arrays and matrices, Files and input/output
streams, Program errors and exception handling, Recursion, Dynamic arrays and
linked lists.
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.
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
This book
emphasizes the role of computer languages as vehicles for expressing knowledge
and it presents basic principles of abstraction and modularity, together with
essential techniques for designing and implementing computer languages.
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.