810. Information Retrieval Systems (3 cr) Lec 3.

Prereq: CSCE 235, 310, or permission

Outline of general information retrieval problem, functional overview of information retrieval. Deterministic models of information retrieval systems; conventional Boolean, fuzzy set theory, p-norm, and vector space models. Probabilistic models. Text analysis and automatic indexing. Automatic query formulation. System-user adaptation and learning mechanisms. Intelligent information retrieval. Retrieval evaluation. New theories and future directions. Practical experience with working experimental information retrieval system.

813. Database Systems (3 cr) Lec 3.

Prereq: CSCE 310

CSCE 813 requires practical experience with a working database system. Data and storage models for database systems; entity/relationship, relational, and constraint models; relational databases; relational algebra and calculus; structured query language; logical database design: normalization, integrity; distributed data storage; concurrency; security issues. Spatial databases and geographic information systems.

821. Foundations of Constraint Processing (3 cr) Lec 3.

Prereq: CSCE 310 and 476/876, or permission

Constraint processing as a powerful formalism for articulating and solving industrial problems such as design, scheduling, and resource allocation. Foundations of constraint satisfaction, its basic mechanisms (e.g., search, backtracking, and consistency-checking algorithms), and constraint programming languages. New directions in the field, such as strategies for decomposition and for symmetry identification.

823. Design and Analysis of Algorithms (3 cr)

Prereq: CSCE 310

Mathematical preliminaries. Strategies for algorithm design, including divide-and-conquer, greedy, dynamic programming and backtracking. Mathematical analysis of algorithms. Introduction to NP-Completeness theory, including the classes P and NP, polynomial transformations and NP-complete problems.

824. Computational Complexity Theory (3 cr) Lec.

Prereq: CSCE 235 and 310

Turing machine model of computation: deterministic, nondeterministic, alternating, probabilistic. Complexity classes: Time and space bounded, deterministic, nondeterministic, probabilistic. Reductions and completeness. Complexity of counting problems. Non-uniformity. Lower bounds. Interactive proofs.

825. Compiler Construction (3 cr) Lec 3.

Prereq: CSCE 310

Review of program language structures, translation, loading, execution and storage allocation. Compilation of simple expressions and statements. Organization of a compiler including compile-time and run-time symbol tables, lexical scan, syntax scan, object code generation, error diagnostics, object code optimization techniques, and overall design.

828. Automata, Computation and Formal Languages (3 cr) Lec 3.

Prereq: CSCE 310

Introduction to the classical theory of computer science. Finite state automata and regular languages, minimization of automata, context free languages and pushdown automata, Turing machines and other models of computation, undecidable problems, introduction to computational complexity.

830. Computer Architecture (3 cr) Lec 3.

Prereq: CSCE 230, 231, 310, and parallel: STAT 880 or ELEC 810; or permission

Credit not applicable towards graduate degree in computer science. Addresses the architecture of single-processor (Von Neumann or SISD) computer systems. Covers the evolution, design, implementation and evaluation of state-of-the-art systems. Topics: Memory Systems, including interleaving, hierarchies, virtual memory and cache implementations; Communications and I/O, including bus architectures, arbitration, I/O processors and DMA channels; and Central Processor Architectures, including RISC and Stack machines, high-speed arithmetic, fetch/execute overlap and parallelism in a single-processor system.

832. High-Performance Processor Architectures (3 cr) Lec 3.

Prereq: CSCE 830; MATH 814; STAT 880 or ELEC 810

CSCE 832 assumes knowledge of computer architecture, pipelining, memory hierarchy, instruction level parallelism, and compiler principles. High performance computing at the processor level. The underlying principles and micro-architectures of contemporary high-performance processors and systems. State-of-the-art architectural approaches to exploiting instruction level parallelism for performance enhancements. Case studies of actual systems highlight real-world trade-offs and theories.

834. VLSI Design (3 cr) Lec 3.

Prereq: CSCE 335

Introductory course in VLSI design using metal-oxide semiconductor (MOS) devices primarily aimed at computer science majors with little or no background in the physics or circuitry of such devices. Design of nMOS and CMOS logic, data-path, control unit, and highly concurrent systems as well as topics in design automation.

835. Cluster and Grid Computing (3 cr) Lec 3.

Prereq: CSCE 310 or equivalent programming experience

CSCE 835 is designed for non-CSE students who have an interest in building or programming cluster to enhance their computationally-intense research. Build and program clusters. Cluster construction, cluster administration, cluster programming and grid computing.

837. File and Storage Systems (3 cr) Lec 3.

Prereq: CSCE 351 or 451/851; CSCE 430/830

CSCE 837 requires designing and implementing a real-life file and storage system. System-level and device-level topics in the design, implementation, and use of file and storage systems. Components and organization of storage systems, disk drive hardware and firmware, multi-disk systems, RAIDs, local, distributed and P2P file systems, and low-power design. Design and implement a real-life file and storage system.

840. Numerical Analysis I (MATH 840) (3 cr) Lec 3.

Prereq: Grade of “Pass” or “C” or better in CSCE 150E or 155/155H; MATH 208/208H

Credit toward the degree may be earned in only one of the following: CSCE/MATH 340/840 and ENGM 480/880. Algorithm formulation for the practical solution of problems, interpolation, roots of equations, differentiation, and integration. Effects of finite precision.

841. Approximation of Functions (MATH 841) (3 cr) Lec 3.

Prereq: A programming language, MATH 821 and 814

Uniform approximation, orthogonal polynomials, least-first-power and least squares approximation, polynomial interpolation and spline interpolation, approximation interpolation by rational functions, and Fourier series.

847. Numerical Analysis II (MATH 847) (3 cr) Lec 3.

Prereq: CSCE 340, MATH 814 and 821

Numerical matrix methods and numerical solutions of ordinary differential equations.

851. Operating Systems Principles (3 cr) Lec 3.

Prereq: CSCE 231 and 310

Credit in CSCE 851 will not count towards a graduate degree in computer science and computer engineering. Organization and structure of operating systems. Control, communication, and synchronization of concurrent processes. Processor and job scheduling. Memory organization and management including paging, segmentation, and virtual memory. Resource management. Deadlock avoidance, detection, recovery. File system concepts and structure. Protection and security. Substantial programming.

855. Distributed Operating Systems (3 cr) Lec 3.

Prereq: CSCE 851

Organization and structure of distributed operating systems. Control, communication, and synchronization of concurrent processes in the context of distributed systems. Processor allocation and scheduling. Deadlock avoidance, detection, recovery in distributed systems. Fault tolerance. Distributed file system concepts and structure. A substantial programming project in distributed systems.

856. Parallel Programming (3 cr) Lec 3.

Prereq: CSCE 310 or equivalent programming experience

Introduction to the fundamentals of parallel computation and applied algorithm design. Methods and models of modern parallel computation; general techniques for designing efficient parallel algorithms for distributed and shared memory multiprocessor machines; principles and practice in programming an existing parallel machine.

857. Systems Administration (3 cr) Lec 3.

Prereq: CSCE 310 or equivalent programming experience

Introduction to basic concepts of system administration. Operating systems and networking overview. User and resource management. Networking, systems and Internet related security. System services and common applications, web services, database services, and mail servers. Basic scripting in SHELL, PERL and EXPECT. Systems administration on a UNIX platform.

862. Communication Networks (3 cr) Lec 3.

Prereq: CSCE 230 and 310; STAT/MATH 380 or STAT 880

Introduction to the architecture of communication networks and the rudiments of performance modeling. Circuit switching, packet switching, hybrid switching, protocols, local and metro area networks, wide area networks and the Internet, elements of performance modeling, and network programming. Advanced material spans network security, asynchronous transfer mode (ATM), optical wireless, cellular and satellite networks, and their performance studies.

864. Internet Systems and Programming (3 cr) Lec 3.

Prereq: CSCE 310

Paradigms, systems, and languages for Internet applications. Client-side and server-side programming, object-based and event-based distributed programming, and multi-tier applications. Coverage of specific technologies varies.

867. Software Quality (3 cr) Lec 3.

Prereq: CSCE 310 or permission

Initial and ongoing software analysis, including metrics, requirements, correctness, performance, testing, and validation. Both frameworks and methods for software quality. Benchmarks and testing, processes for quality assurance, performance and quality models, software quality tools, testable designs, and automated testing.

870. Computer Graphics (3 cr) Lec 3.

Prereq: CSCE 310; MATH 814; or permission

Display and recording devices; incremental plotters, point, vector, and character generation; grey scale displays, digitizers and scanners; digital image storage; interactive and passive graphics; pattern recognition; data structures and graphics software; the mathematics of three dimensions; homogeneous coordinates; projections and the hidden-line problem.

871. Introduction to Bioinformatics (3 cr) Lec 3.

Prereq: CSCE 310; STAT/MATH 380 or STAT 880

Fundamentals and current trends in bioinformatics. Scoring matrices and pairwise sequence alignments via dynamic programming, BLAST, and other heuristics. Multiple sequence alignments. Applications of machine learning methods such as hidden Markov models and support vector machines to biological problems such as family modeling and phylogeny.

872. Digital Image Processing (3 cr) Lec 3.

Prereq: CSCE 156 or permission

Digital imaging systems, digital image processing, and low-level computer vision with emphasis in data structures, algorithms, and system analysis and modeling. Digital image formation and presentation, images statistics and descriptions, operations and transforms, and system simulation. Applications include system design, restoration and enhancement, reconstruction and geometric manipulation, compression, and low-level analysis for computer vision.

873. Computer Vision (3 cr) Lec 3.

Prereq: CSCE 156 or permission

High-level processing for image understanding and high-level vision with an emphasis on data structures, algorithms, and modeling. Low-level representation, basic pattern-recognition and image-analysis techniques, segmentation, color, texture and motion analysis, and representation of 2-D and 3-D shape. Applications include content based image retrieval, digital libraries, and interpretation of satellite imagery.

874. Introduction to Data Mining (3 cr) Lec 3.

Prereq: CSCE 310; STAT/MATH 380 or STAT 880

CSCE 874 requires a project involving application of data mining techniques to real-world problems. Data mining and knowledge discovery methods and their application to real-world problems. Algorithmic and systems issues. Statistical foundations, association delivery, classification, prediction, clustering, spatial data mining and advanced techniques.

875. Multiagent Systems (3 cr) Lec 3.

Prereq: CSCE 310

Distributed problem solving and planning, search algorithms for agents, distributed rational decision making, learning multiagent systems, computational organization theory, formal methods in Distributed Artificial Intelligence, multiagent negotiations, emergent behaviors (such as ants and worms), and Robocup technologies and real-time coalition formation.

876. Introduction to Artificial Intelligence (3 cr) Lec 3.

Prereq: CSCE 310

Introduction to basic principles, techniques, and tools now being used in the area of machine intelligence. Languages for AI programming introduced with emphasis on LISP. Problem solving, search, game playing, knowledge representation, expert systems, and applications.

877. Cryptography and Computer Security (3 cr) Lec 3.

Prereq: CSCE 310, MATH 814 or equivalent

Introductory course on cryptography and computer security. Classical cryptography (substitution, Vigenere, Hill and permutation ciphers, and the one-time pad); Block ciphers and stream ciphers; The Data Encrytion Standard; Public-key cryptography, including RSA and El-Gamal systems; Signature schemes, including the Digital Signature Standard; Key exchange, key management and identification protocols.

878. Introduction to Machine Learning (3 cr) Lec 3.

Prereq: CSCE 310

STAT 380/880 recommended. Introduction to the fundamentals and current trends in machine learning. Applications for game playing, text categorization, speech recognition, automatic system control, data mining, computational biology, and robotics. Decision trees, artificial neural networks, Bayesian classifiers, genetic algorithms, and instance based classifiers.

879. Introduction to Neural Networks (3 cr) Lec 3.

Introduction to the concepts, design and application of connection-based computing begins by simulating neural networks. Competing alternative network architectures, including sparse distributed memories, Hopfield networks, and the multi-layered feed-forward systems. Construction and improvement of algorithms used for training of neural networks addressed to reduce training time and improve generalization. Algorithms for training and synthesizing effective networks are implemented in high level language programs running on conventional computers. Methods for synthesizing and simplifying network architectures for improved generalization. Pattern recognition, computer vision, robotics medical diagnosis, weather and economic forecasting.

*891. Internship in Computer Practice (1 cr) Fld.

A detailed project proposal must be prepared by the student and approved by the department prior to the start of the project. A final report must be submitted. Experiential learning in conjunction with an approved industrial or governmental agency under the joint supervision of an outside sponsor and a faculty member.

896. Special Topics in Computer Science (1-3 cr per sem, max 24) Lec.

Aspects of computers and computing not covered elsewhere in the curriculum.

*897. Masters Project (1-6 cr)

Prereq: Permission of adviser

Designed for students pursuing a non-thesis option (Option III) to work on a project under the supervision of a member of the computer science and engineering faculty.

898. Computer Problems (3 cr)

Independent project executed under the guidance of a member of the Computer Science faculty. Solution and documentation of a computer problem demanding a thorough knowledge of either the numerical or nonnumerical aspects of computer science.

*899. Masters Thesis (6-10 cr)

Prereq: Admission to masters degree program and permission of major adviser

910. Information Organization and Retrieval (3 cr)

Prereq: CSCE 810

Aspects of natural language processing on digital computers. Analysis of information content by statistical, syntactic, and logical methods. Search and matching techniques. Automatic retrieval systems, question-answering systems. Evaluation of retrieval effectiveness.

913. Advanced Topics in Database Systems (3 cr)

Prereq: CSCE 813

Database system topics, coverage varying from year to year. Examples: Normalization theory; statistical databases; distributed databases; failure recovery; implementation issues. Readings in the current literature.

914. Constraint Database Systems (3 cr) Lec.

Prereq: CSCE 813 or 913 and permission

Introduction to constraint database systems. Constraint data model, constraint query languages, query optimization and evaluation, constraint data storage and applications. Assignments in both use and the implementation of systems.

923. Development and Analysis of Efficient Algorithms (3 cr)

Prereq: CSCE 820 and 827

Analysis of performance of algorithms on random access machines and Turing machines, data structures for design of efficient algorithms, sorting algorithms, divide and conquer strategies, algorithms on graphs and their performance bounds, pattern matching algorithms, achievable lower bounds on complexity, NP complete problems.

924. Graph Algorithms (3 cr)

Prereq: CSCE 827, MATH 852, or permission

Review concepts related to analysis of algorithms and graph theory. Classical graph theoretic algorithms including Eulerian paths, Hamiltonian circuits, shortest paths, network flows and traveling salesman. Planar graph algorithms. Theory of alternating chains and algorithms for graph matching problems. Approximate and parallel algorithms. Applications of graph algorithms to engineering and physical sciences.

925. Scheduling Theory (3 cr)

Prereq: Permission

Scheduling theory with particular emphasis to its application in computer science. Polynomial-time algorithms, NP-hardness proofs and analysis of heuristics. Minimization of makespan and mean flow time. Real-Time scheduling.

930. Advanced Computer Architecture (3 cr)

Prereq: CSCE 830

Recent advances in computer architecture including the effects of VLSI and methods of improving performance. Parallelism, pipelining, vector and array processors, multiprocessors and distributed processors, and data-flow architectures.

932. Fault-Tolerance: Testing and Testable Design (3 cr)

Prereq: CSCE 834 or permission

Increasing density of microelectronic circuits makes them harder to test during production and field operation. Theory and techniques developed to solve this problem. Faults and fault modeling; algorithms for test generation and fault simulation; built-in-self-test methods and standards; design for testability; and self-checking circuits.

933. Fault-Tolerance: System Design and Analysis (3 cr)

Prereq: CSCE 830 or permission

Theory and practice of creating extremely dependable digital systems through online fault-tolerance. Emphasizes modular redundancy in hardware and software to permit detection, masking, and removal of faulty components. Case studies from aerospace, banking, and other disciplines. Fault classification, error detection and diagnosis, dependability metrics, Byzantine Agreement, design trade-offs, and system simulation and modeling (esp. Markov).

942. Numerical Analysis III (MATH 942) (3 cr)

Prereq: CSCE/MATH 840 or 841 or 847 or permission

Advanced topics in numerical analysis.

952. Advanced Computer Networks (3 cr)

Prereq: CSCE 862

Advanced level course on the recent development in computer networks. Integrated Services Digital Networks (ISDN), Broadband-ISDN and Asynchronous Transfer Mode (ATM), Multimedia Source and Traffic Characteristics, Source Policing, Scheduling and Quality of Service, Wireless Communication, Tracking of Mobile Users, Performance Computer networks.

953. Optical Communication Networks (3 cr) Lec 3.

Prereq: CSCE 462/862 or equivalent

State-of-the-art optical communication networks, encompassing traditional networks operating on optical fiber and next-generation networks such as wavelength division multiplexed (WDM) and optical time division multiplexed (OTDM) networks. Fundamentals of optical network design, control, and management. Optical network design and modeling, routing and wavelength assignment algorithms, optical network simulation tools and techniques.

961. Coding Theory (3 cr)

Prereq: MATH 817 desirable

Channels, introduction to information theory, Shannon’s fundamental theorem, Linear codes, Hamming codes, Reed-Muller codes, cyclic codes, idempotents, BCH codes, Reed-Solomon codes, Quadratic residue codes, perfect single-error correcting codes, Sphere packings, the Golay codes, Lloyds theorem, nonexistence theorems, weight enumerators, the MacWilliams equation, association schemes, quasi-symmetric designs, polarities of designs, extension of graphs, self-orthogonal codes and designs.

962. Advanced Software Engineering (3 cr)

Prereq: CSCE 361

Recent advances in the field of software engineering. Software reuse, artificial intelligence approaches to software design, usability and requirements engineering, and design environments. Computer tools for the design of software products. Readings from current software engineering literature discussed and evaluated. Students will participate in a group project which investigates specific software engineering research topics.

963. Software Process Engineering (3 cr) Lec 3.

Prereq: CSCE 361 or permission

Engineering of the software development process including software life-cycle, maturity models, process programming, and process management. Both theory and practice of engineering large, long-lived software systems. Process analysis, modeling, workflows, standards, process environments and tools, automation, and organizational context. Case studies illuminate the application of software process theory to engineering practice. Teams analyze and develop software management plans and tools.

966. Software Architecture and Frameworks (3 cr) Lec 3.

Prereq: CSCE 866 or permission

Architectural aspects of software development including design patterns, frameworks, standardization of architectures and components, and development environments. Methodologies for creating reusable solutions for common problems in a variety of application areas. Experience in the development and use pattern catalogs and design standards.

970. Pattern Recognition (3 cr)

Prereq: MATH 814, STAT 880, CSCE 810, or equivalent

Introduction to statistical decision theory, adaptive classifiers, supervised and nonsupervised training. Pattern recognition systems: transducers, feature extractors, decision units. Applications to optical character recognition, speech processing, remote sensing.

971. Advanced Bioinformatics (3 cr) Lec 3.

Prereq: CSCE 471/871

Advanced algorithmic techniques for bioinformatics. Development and analysis of string matching, graph theoretic and dynamic programming techniques applied to systems and computational biology problems such s multiple sequence alignment, alignment of DNA and protein sequences, genome rearrangements, and phylogeny and haplotypes.

973. Support Vector Machines (3 cr) Lec 3.

Prereq: CSCE 310; STAT/MATH 380 or STAT 880; MATH 314/814

Core theory of the machine learning technique called support vector machines. Margin, kernels, and the formulation of a machine learning problem as an optimization problem that can be solved optimally. Implementation issues, kernel design, the appropriateness of various kernels for different applications, and regularization.

974. Genetic Algorithms (3 cr)

Prereq: CSCE 310 and 876

For students taking this course, no biological sciences background is needed. However, a knowledge of genetic principles may help student to improve current algorithms. Introduction of the motivation and current implementations of advanced genetic algorithms. These algorithms are built on basic principles borrowed from biology. Illustrates how a novel, implicitly-parallel search is implemented to obtain solutions for combinatorically-difficult problems.

976. Artificial Intelligence (3 cr)

Prereq: CSCE 876 or equivalent

For students with some sophistication and considerable interest in exploring methods of designing and using algorithms useful for finding adequate answers to combinatorically large problems that require largely symbolic rather than numeric computing. It will be assumed students are highly proficient in one or more high-level computer languages and either are or will be able to function in functional and descriptive languages such as LISP and PROLOG. Study, analyze and critique basic and current research papers and to engage in artificial intelligence projects and experiments either alone or in small groups. Artificial intelligence environments, tools and expert system building. Class participation will be encouraged for the review of the more recent AI literature.

977. Data Encryption (3 cr)

Prereq: STAT 880, CSCE 235 or MATH 817 or permission

History of public cryptology; elements of statistics, combinatorics, number theory, group theory; symmetric and asymmetric cryptosystems, “trap door” functions; public key cryptosystems; RSA and knapsack; levels of cryptographic security; computational complexity of algorithms; National Bureau of Standards-DES ( Standard); block and stream cyphers; cypher key management; protection of proprietary software and data.

979. Advances in Neural Networks and Genetic Algorithms (3 cr) Lec.

Graduate level course requires reading, research, and programming selected to address the open problems of improving the speed and robustness of algorithms for learning in networks and other self-organizing systems. The state-of-the-art methods for supervised training of neural networks followed by the implementation and application of genetic algorithms. Evolution and self-organization of complex, adaptive, nonlinear systems for solving problems of pattern recognition, cognition, and control. Obtaining insight into the internal workings of neural networks. Current theories and experimental testing used for analysis and testing of connections and thresholds of trained neural networks. Reference materials include research reports, papers, and books on the theory and design of neural network based processors and problem solving systems.

990. Seminar (1-3 cr, max 24) Lec.

Prereq: Permission

Frontiers of an area of computer science.

996. Research Problems Other Than Thesis (1-6 cr)

Investigation of minor research problems to introduce graduate students to the methods of research in computer science by assigning a problem which is of research interest but within the capacity of a graduate student to complete within a semester.

999. Doctoral Dissertation (1-24 cr, max 55)

Prereq: Admission to doctoral degree program and permission of supervisory committee chair