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Course Criteria
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3.00 Credits
Randomized Algorithms. (3-0). Credit 3. This course gives an introduction to randomized algorithms; selected tools and techniques from probability theory and game theory are reviewed, with a view towards algorithmic applications; the main focus is a thorough discussion of the main paradigms, techniques, and tools in the design and analysis of randomized algorithms; a detailed analysis of numerous algorithms illustrates the abstract concepts and techniques. Prerequisite: Graduate classification.
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3.00 Credits
Parallel/Distributed Numerical Algorithms and Applications. (3-0). Credit 3. A unified treatment of parallel and distributed numerical algorithms; parallel and distributed computation models, parallel computation of arithmetic expressions; fast algorithms for numerical linear algebra, partial differential equations and nonlinear optimization. Prerequisites: CSCE 653; MATH 304. Cross-listed with ECEN 659.
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3.00 Credits
Computational Linear Algebra. (3-0). Credit 3. Techniques in matrix computation: elimination methods, matrix decomposition, generalized inverses, orthogonalization and least-squares, eigenvalue problems and singular value decomposition, iterative methods and error analysis. Prerequisite: CSCE 442 or equivalent or MATH 417 or equivalent. Cross-listed with MATH 660.
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3.00 Credits
Integrated Systems Design Automation. (3-0). Credit 3. VLSI design systems and their levels of abstracting; algorithms for general VLSI design and implementation; computer aided design tools and principles; physical and logical models. Prerequisite: Graduate classification.
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3.00 Credits
Distributed Processing Systems. (3-0). Credit 3. Principles and practices of distributed processing; protocols, remote procedure calls; file sharing; reliable system design; load balancing; distributed database systems; protection and security; implementation. Prerequisite: CSCE 313 and 463 or CSCE 612.
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3.00 Credits
Real-Time Systems. (3-0). Credit 3. Taxonomy of real-time computer systems; scheduling algorithms for static and dynamic real-time tasks; hard real-time communications protocols; programming languages and environments for real-time systems; case studies of real-time operating systems. Prerequisites: CSCE 313, and 463 or 611, or approval of instructor.
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3.00 Credits
Advanced Networking and Security. (3-0). Credit 3. Security aspects of various network protocols including investigation and tool development using "live" machines and networks. Prerequisites: Graduate classification and approval of instructor.
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3.00 Credits
Pattern Analysis. (3-0). Credit 3. Introduction to methods for the analysis, classification and clustering of high dimensional data in Computer Science applications. Course contents include density and parameter estimation, linear feature extraction, feature subset selection, clustering, Bayesian and geometric classifiers, non-linear dimensionality reduction methods from statistical learning theory and spectral graph theory, Hidden Markov models, and ensemble learning. Prerequisites: MATH 222, MATH 411 (or equivalent) and graduate classification.
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3.00 Credits
Collaborative Systems and Models. (3-0). Credit 3. Collaborative systems support group activities over computer networks; emphasis on human factors, system design is different from traditional systems; overviews existing research efforts to address various design issues; state-of-the-art knowledge and how to implement collaborative applications. Prerequisites: CSCE 310 or 603, 313 or 611, a program language (C++/JAVA) and CSCE 436 or 671 or 672 or approval of instructor and graduate classification.
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3.00 Credits
Distributed Algorithms and Systems. (3-0). Credit 3. Introduction to fundamental algorithmic results in distributed computing systems; leader election, mutual exclusion, consensus, logical time and causality, distributed snapshots, algorithmic fault tolerance, shared memory, clock synchronization. Prerequisites: CSCE 411 or equivalent or approval of instructor.
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