Having a Glossary meant I could reduce the text on most pages, while expanding background for the definitions, and relating the ideas to other similar, contradictory, or more basic ideas. Why Bother with Definitions? The value of a definition is insight. Simple descriptions are not always possible.
None This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python.
No knowledge of programming is assumed. None Students study the principles of relational database design and learn to build, populate, manipulate and query databases using SQL on datasets relevant to their interests.
Students will also explore data presentation through data visualization. None This course uses a project-based learning approach towards the study of web technologies and web programming. Students study the principles of web design and each student builds one or more interactive websites based on content relevant to their scholarly interest in the humanities or social sciences.
None An accelerated introduction to the fundamental concepts of computer science for students who lack a formal background in the field.
Topics include algorithm design and program development; data types; control structures; subprograms and parameter passing; recursion; data structures; searching and sorting; dynamic storage allocation and pointers; abstract data types, such as stacks, queues, lists, and tree structures; generic packages; and an introduction to the principles of object-oriented programming.
The primary programming language used in the course will be Java. Students should expect an average of hours of programming and related course work per week.
This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms.
Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.
None An introduction to theory, computational techniques, and applications of linear algebra, probability and statistics. These three areas of continuous mathematics are critical in many parts of computer science, including machine learning, scientific computing, computer vision, computational biology, natural language processing, and computer graphics.
The course teaches a specialized language for mathematical computation, such as Matlab, and discusses how the language can be used for computation and for graphical output. No prior knowledge of linear algebra, probability, or statistics is assumed. Students taking this class should already have substantial programming experience.
Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. Multivariate calculus and linear algebra.
Some programming experience recommended. Methods for numerical applications in the physical and biological sciences, engineering, and finance.
Basic principles and algorithms; specific problems from various application areas; use of standard software packages.
This is a capstone course based on compilers and modern programming languages. The topics covered include structure of one-pass and multiple-pass compilers; symbol table management; lexical analysis; traditional and automated parsing techniques, including recursive descent and LR parsing; syntax-directed translation and semantic analysis; run-time storage management; intermediate code generation; introduction to optimization; and code generation.
The course includes a special compiler-related capstone project, which ties together concepts of algorithms, theory formal languagesprogramming languages, software engineering, computer architecture, and other subjects covered in the MS curriculum.
This project requires a substantial semester-long programming effort, such as construction of a language compilation or translation system that includes lexical and syntactic analyzers, a type checker, and a code generator. No prior experience in the financial sector domain is required. The theme of this course is an "applied case study" and focuses on fixed income markets.
Topics covered include an overview of the markets, the inner workings of an investment bank, the market players, and where software engineers fit in.
Students will be grouped into small teams to build a financial application using practical software engineering principles. Each team will build a risk management framework, starting with basic components. In particular, we will present an overview of the Linux operating system, command line tools find, grep, sedprogramming tools GIT, Eclipse, DTraceweb and database tools Apache, MySQLand system administration tools.
We will also cover scripting languages such as shell and Python. Students must have a working knowledge of fundamental data structures and associated algorithms.
For some of the practical aspects of the course, a working knowledge of an object-oriented programming language e. An undergraduate course in data communication and networks is helpful but not required.
This course teaches the design and implementation techniques essential for engineering robust networks.Title Authors Published Abstract Publication Details; Easy Email Encryption with Easy Key Management John S.
Koh, Steven M. Bellovin, Jason Nieh. This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python. No knowledge of programming is assumed. Not open to graduate Computer Science, Information Systems, Mathematics or Scientific.
Hyperlinked definitions and discussions of many terms in cryptography, mathematics, statistics, electronics, patents, logic, and argumentation used in cipher construction, analysis and production.
A Ciphers By Ritter page. Hyperlinked definitions and discussions of many terms in cryptography, mathematics, statistics, electronics, patents, logic, and argumentation used in cipher construction, analysis and production.
A Ciphers By Ritter page. Title Authors Published Abstract Publication Details; Analysis of the CLEAR Protocol per the National Academies' Framework Steven M. Bellovin, Matt Blaze, Dan Boneh, Susan Landau, Ronald L. Rivest. This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python.
No knowledge of programming is assumed. Not open to graduate Computer Science, Information Systems, Mathematics or Scientific.
|Ritter's Crypto Glossary and Dictionary of Technical Cryptography||On the one hand, some people claim it can be accomplished safely; others dispute that. In an attempt to make progress, a National Academies study committee propounded a framework to use when analyzing proposed solutions.|
|Having a Glossary meant I could reduce the text on most pages, while expanding background for the definitions, and relating the ideas to other similar, contradictory, or more basic ideas.|
|Technical Reports | Department of Computer Science, Columbia University||Bellovin, Jason Nieh Email privacy is of crucial importance. Existing email encryption approaches are comprehensive but seldom used due to their complexity and inconvenience.|