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Course Criteria
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3.00 Credits
P: 40200. Introduction to programmable logic; elements of microprocessor system design; interrupt structures; interfacing using LSI devices; hardware timers; interactive debugging; physical device I/O programming; vectored and polled service; microprocessor architecture; and self-paced laboratory using A/D converters, D/A converters, etc.
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3.00 Credits
P: CSCI 36200 and STAT 41600 or STAT 51100. Biometrics is capturing and using physiological and behavioral characteristics for personal identification. It is set to become the successor to the PIN. This course will introduce computational methods for the implementation of various biometric technologies including face and voice recognition, fingerprint and iris identification, and DNA matching.
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3.00 Credits
P: CSCI 36200. Fall. Tools and techniques used in software development. Lifecycle concepts applied to program specification, development, and maintenance. Topics include overall design principles in software development; the use of structured programming techniques in writing large programs; formal methods of program verification; and techniques and software tools for program testing, maintenance, and documentation. A primary goal of this course is to provide experience in team development of software.
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3.00 Credits
P: CSCI 36200. Spring. Introduction to the object-oriented paradigm in software development. Basic concepts: objects, classes, messaging, inheritance, and methodologies. Analysis: defining objects, structures, attributes, and services. Design: transforming the analytic model into the design model. Implementation: comparison of the support features provided by languages such as Smalltalk, C++, Eiffel, and CLOS. A significant design project is required.
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3.00 Credits
P: 36200. Techniques for analyzing and comparing algorithms. Average case analysis in sorting and searching; dynamic programming: greedy algorithms, amortized analysis, and applications; matrix algorithms: polynomials, discrete Fourier transforms, and fast Fourier transforms, parallel algorithms: examples in sorting, searching, graphs, and matrices, computational complexity, polynomial complexity classes P, NP.
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3.00 Credits
P: 36200. Fall. Introduction to formal languages and automata theory: finite automata and regular expressions, context-free grammars and languages, pushdown automata, equivalence of CFGs and pushdown automata, application of pushdown automata in parsing, closure properties, pumping lemmas, decision procedures, Turing machines, computability, undecidability, and a brief survey of the Chomsky hierarchy.
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3.00 Credits
P: 23000 and MATH 35100. P or C: MATH 26200. Fall. Solving scientific problems on computers. Languages for scientific computing. Software development on workstations: using tools the environment provides, organization of programs. Computer architecture: impact on software and algorithms. Problem formulation: model selection/simplification, relationship to numerical methods. Solution of linear equations: methods and packages. Nonlinear equations and optimization problems.
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3.00 Credits
P: 47500. Spring. Elementary statistical computing: time series analysis, model fitting, robust methods, generation of pseudorandom numbers, and Monte Carlo methods. Interpolation and curve fitting; numerical integration. Solving ordinary differential equations. Use of packaged environments and symbolic computation for scientific purposes.
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3.00 Credits
P: 47600. Fall. Architecture of supercomputers: pipelined, vector, SIMD, MIMD; implications for algorithm and program design; and vectorization, parallelization, loop restructuring, and nonstandard language features. Splitting computation between supercomputers and workstations; interactive analyses of remote machines' output. Numerical methods for large-scale problems: examples from continuum mechanics, graphical visualization, and statistical computing. A project is required.
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3.00 Credits
P or C: 24000, MATH 35100/51100, STAT 51100/41600. An introduction to data warehousing and OLAP technology for data mining, data processing, languages and systems, and descriptive data mining: characterization and comparison, association analysis classification and predication, cluster analysis mining complex types of data, application, and trends in data mining.
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