University Catalog 2005-2006

Computer Science (CS)

Computer Science

105 Computer Ethics and Society (1:1:0)Prerequisite: 12 credits of undergraduate course work. Intensive introduction to legal, social, and ethical issues surrounding software development and computer use. Stresses professional conduct, social responsibility, and rigorous standards for software testing and reliability. Examines issues such as liability, ownership of information, privacy, security, and crime. Students read, write, discuss, and present reports on these topics.

112 Computer Science I (4:3:2) Prerequisites: thorough understanding of high school algebra and trigonometry, and successful completion of math placement test offered through the Testing Center; or a grade of C or better in MATH 105. Introduction to computer science for majors and others with serious interest in computer science. Topics include overview of computer system hardware and organization, problem-solving methods and algorithm development, program structures, abstract data types, simple data and file structures, introduction to analysis of algorithmic complexity and program correctness, and applications development in a high-level programming language that supports modular design.

211 Computer Science II (3:3:0) Prerequisite: grade of C or better in CS 112. Continuation of CS 112. Topics include abstract data types and data structures (sets, files, strings, linked lists, stacks, queues, trees, graphs) and examples of their applications. Emphasis on program development continues and is reinforced through several larger programming projects. Additional programming language instruction supplements major topics.

261 Introduction to a Second Language (1:1:0) Prerequisite: grade of C or better in CS 211. Not available for CS major credit. Advanced programming, using the Java programming language. Other languages may be offered at times.

265 Assembly Language Programming (3:3:0) Prerequisite: grade of C or better in CS 211; corequisite: CS 105. Symbolic assembly language and computer structures; arithmetic and logical operations; machine representations of numbers, characters, and instructions; input-output and data conversions; addressing techniques; assembler directives; subroutine linkage; and macroprocessing.

305 Ethics and Law for the Computing Professional (3:3:0) Prerequisite: CS 105 or IT 103; junior standing (at least 60 credits). Practical course to assist students in becoming effective computer professionals by examining the challenging legal and ethical issues surrounding computer technology and its use, and building foundation to deal with those challenges. Applies philosophical bases for ethical decision-making to modern concerns raised by computers and technology. Addresses topics covered by CS 105 in a more intensive manner, and focuses on emerging legal and ethical issues involved in e-commerce and the widespread use of Internet.

306 Synthesis of Ethics and Law for the Computing Professional (3:3:0) Prerequisites: CS 105 or IT 103; junior standing; completion or concurrent enrollment in all required general education courses. For course description, see CS 305. Computer science majors may use this course to satisfy the general education synthesis requirement, as long as they have not previously taken CS 305 for credit.

310 Computer Science III (3:3:0) Prerequisite: grade of C or better in CS 211. Tools and techniques required to develop moderate to large programs. Topics include continued study of object-oriented techniques, data structures, recursion, and problem-solving skills. Students complete several moderate-size programs.

330 Formal Methods and Models (3:3:0) Prerequisites: grade of C or better in CS 211 and MATH 125. Abstract concepts that underlie much advanced work in computer science, with major emphasis on formal languages, models of computation, logic, and proof strategies.

332 Object-Oriented Specification and Implementation (3:3:0) Prerequisite: grade of C or better in CS 310. Concentration on the transition from an abstract data type (ADT) specification to its implementation. Covers symbolic logic for reasoning about programs, axiomatic and algebraic methods for ADT specification, and introduction to goal-directed programming. Term project involves the design and construction of a program incorporating several ADTs.

363 Comparative Programming Languages (3:3:0) Prerequisite: grade of C or better in CS 365. Key programming mechanisms described independently of particular machines or languages including control, binding, procedural abstraction, and types. Systematically surveys diverse high-level language capabilities.

365 Computer Systems Architecture (3:3:0) Prerequisites: grade of C or better in ECE 303. Computer hardware organization, software structure, and data organization. Students complete term project that simulates one computer system on another.

367 Computer Systems and Programming (3:3:0) Prerequisite: grade of C or better in either ECE 303 or 445. Uses bottom-up approach to teach how high-level language control and data structures are represented at the machine level. Introduces systems programming.

391 Advanced Programming Lab (1:0:1) Corequisite: grade of C or better in CS 310 and permission of instructor. Programming-intensive lab course. Students refine problem-solving and programming skills while gaining experience in teamwork. Focuses on data structures, recursion, backtracking, dynamic programming, and debugging. Central focus is application of familiar and new algorithms and data structures to novel circumstances.

421 Introduction to Software Engineering (3:3:0) Prerequisites: grade of C or better in CS 310 (or both CS 211 and SYST 301) and ENGL 302. Techniques in software design and development. Discusses formal models of structured programming, software engineering methods and tools, functional or object-oriented design, and documentation. Working in teams, students organize, manage, and develop software engineering project.

440 Language Processors and Programming Environments (3:3:0) Prerequisites: grade of C or better in CS 310, 330 and 365. Survey of basic programming language processors and software development tools such as assemblers, interpreters, and compilers. Topics include design and construction of language processors, formal syntactic definition methods, parsing techniques, and code generation techniques.

450 Database Concepts (3:3:0) Prerequisite: grade of C or better in CS 310 and 330. Data models and data sub-languages for the relational, hierarchical, and network approaches to database management systems. Covers normal forms, external models, implementation, data independence, alternative logical views of data, and object-oriented design. Various approaches are compared.

451 Computer Graphics and Software Design (3:3:0) Prerequisites: grade of C or better in MATH 203, and CS 310 and 365. Basic graphics principles and programming. Topics include graphics hardware, graphical user interfaces, scan conversion, transformations, viewing, hidden surface removal, illumination, and graphics software design and techniques.

455 Computer Networking Systems (3:3:0) Prerequisites: grade of C or better in CS 310 and 365, and STAT 344. Data communications and networking protocols, with study organized to follow the layers of the Internet Protocol Suite (the TCP/IP family of protocols) Topics include role of various media and software components, local and wide area network protocols, network performance, and emerging advanced commercial technologies.

471 Operating Systems (3:3:0) Prerequisites: grade of C or better in CS 310 and 365. Issues in multiprogramming. Covers concurrent processes and synchronization mechanisms, processor scheduling, memory management, file management, I/O management, deadlock management, performance of operating systems, and projects dealing with synchronization in a multiprogrammed OS and with virtual memory management. f,s

475 Concurrent and Distributed Systems (3:3:0) Prerequisite: grade of C or better in CS 471 or permission of instructor. Practical issues in designing and implementing concurrent and distributed software. Topics include concurrent programming, synchronization, multithreading, local and wide-area network protocols, distributed computation, systems integration, and techniques for expressing coarse-grained parallelism at the application level. Projects involve network programming at the application level.

480 Introduction to Artificial Intelligence (3:3:0) Prerequisites: grade of C or better in CS 310 and 330. Principles and methods for knowledge representation, reasoning, learning, problem solving, planning, heuristic search, and natural language processing and their application to building intelligent systems in a variety of domains. LISP, PROLOG, or an expert system programming language is used. f,s

482 Computer Vision(3:3:0) Prerequisite: grade of C or better in MATH 203, STAT 344, and CS 310. Basic principles of visual perception and their implementation on computer systems. Topics include early visual processing, edge detection, segmentation, intrinsic images, image modeling, representation of visual knowledge, and image understanding. Students complete projects involving real images.

483 Data Structures and Analysis of Algorithms (3:3:0) Prerequisites: grade of C or better in CS 310 and 330, and MATH 114. Analyzes computational resources required for important problem types by alternative algorithms and their associated data structures, using mathematically rigorous techniques. Specific algorithms analyzed and improved. f,s

490 Design Exhibition (3:3:0) Prerequisites: grade of C or better in CS 421, 483; two other CS 400-level courses; and senior standing. Capstone course focusing on design and successful implementation of a major software project, encompassing a broad spectrum of knowledge and skills, developed by team of students. Final exhibition to faculty/industry panel required. f,s

498 Independent Study in Computer Science (1-3:0:0) Prerequisites: 60 credits, major in computer science, and permission of instructor. Research and analysis of selected problems or topics in computer science. Topic must be arranged with an instructor and approved by department chair before registering. May be repeated for a maximum of 6 credits if topics are substantially different.

499 Special Topics in Computer Science (3:3:0) Prerequisites: 60 credits and permission of instructor; specific prerequisites vary with nature of topic. Topics of special interest to undergraduates. May be repeated for a maximum of 6 credits if topics are substantially different.

540 Language Processors (3:3:0) Prerequisites: MATH 125; CS 265, 310, and 330. Basic programming language processors such as assemblers, interpreters, and compilers. Topics include design and construction of language processors, formal syntactic definition methods, parsing techniques, and code generation techniques. Lab includes construction of language processors, and experience with programming environments.

571 Operating Systems (3:3:0) Prerequisites: CS 310 and 365. Models of operating systems. Major functions including processes, memory management, I/O, interprocess communication, files, directories, shells, distributed systems, performance, and user interface.

580 Introduction to Artificial Intelligence (3:3:0) Prerequisites: CS 310 and 330. Principles and methods for knowledge representation, reasoning, learning, problem solving, planning, heuristic search, and natural language processing and their application to building intelligent systems in a variety of domains. Uses LISP, PROLOG, or an expert system programming language.

583 Analysis of Algorithms (3:3:0) Prerequisites: CS 310 and 330, and MATH 125. Topics include the analysis of sequential and parallel algorithmic strategies (such as greedy methods, divide and conquer strategies, dynamic programming, search and traversal techniques, approximation algorithms), analysis of specific algorithms falling into these classes, NP-Hard and NP-Complete problems.

631 Object-Oriented Design Patterns (3:3:0) Prerequisite: SWE 619 or 620, or CS 540 or 571, or graduate course in object-oriented programming or equivalent. Principles of object-oriented design through design patterns. Studies selection of appropriate object-oriented structure after system requirements or requirements specification of software system have been developed. Design patterns are created in the logic view of the software system. Studies generalized design solutions for generalized software design problems, and reuse of design patterns. Once developed, design patterns may be specified in any object-oriented language.

635 Foundations of Parallel Computation (3:3:0) Prerequisites: CS 583 and 540 or 571, or equivalent. Survey of the field of parallel computation. Three major parallel computing paradigms (MIMD computation, SIMD computation, and data flow computation) are covered. Emphasizes interfaces between algorithm design and implementation, architecture, and software. Parallel algorithms and parallel programming languages examined relative to architecture of particular parallel computers.

640 Advanced Compilers (3:3:0) Prerequisites: CS 540 and 583 or equivalent. Examines advanced compiler techniques such as code optimizations for sequential and parallel machines; compilers for logical, functional, or object oriented languages; and other topics in current literature.

650 Database Engineering (3:3:0) Prerequisites: CS 540, 583, and 571. Data models for network, hierarchical, object-oriented, and relational management information systems. Covers development (including internal structures) of a database system.

652 Computer Graphics (3:3:0) Prerequisite: CS 583. Graphics principles and programming. Topics include graphics hardware, antialiasing, transformations, viewing, illumination, blending, texture mapping, color models, curves, surfaces, and animation.

656 Computer Communications and Networking (3:3:0) Prerequisites: CS 571 and STAT 344 or equivalent. Techniques and systems for communication of data between computational devices and layers of Internet Protocol Suite. Topics include the role of various media and software components, local and wide area network protocols, network design, performance and cost considerations, and emerging advanced commercial technologies. Emphasizes TCP/IP family of protocols.

667/IT 667 Biometrics (3:3:0) Prerequisites: CS580 or permission of the instructor. Basic principles and methods for automatic authentication of individuals. Technologies include face, fingerprint and iris recognition, and speaker verification. Additional topics cover multimodal biometrics, system design, performance evaluation, and privacy issues. Term project required.

668 Computer Architecture Systems (3:3:0) Prerequisite: CS 571 or 540 or equivalent. Examines principles and practices relating computer architecture to programming execution and efficiency. Presents new approach that stresses performance and cost of architecture. Examines principles of compiler and OS implications, instructions sets, basic processors, pipelines, and memory-hierarchy. Topics may include RISC machines, cache memories, register usage, VAX architecture, and vector machines.

671 Advanced Operating Systems (3:3:0) Prerequisite: CS 571 or permission of instructor. Advanced topics in design and implementation of microkernel-based, object-oriented, and distributed operating systems. Specific topics include support for interprocess communication, interaction between computer architecture and operating systems, distributed file systems, transactions, and distributed shared memory.

672 Computer System Performance Evaluation (3:3:0) Prerequisites: CS 571 and MATH 351 or permission of instructor. Theory and practice of analytical models of computer systems. Topics include queuing networks, single and multiple class mean-value analysis, models of centralized and client-server systems, software performance engineering, and web servers performance.

673 Multimedia Computing and Systems (3:3:0) Prerequisite: CS 571. Focuses on technological and development environments in developing multimedia applications. Projects involve experience with multimedia authoring tools and simulations to assess performance.

680 Natural Language Processing (3:3:0) Prerequisites: CS 540 and 580. Explores principles of designing computer programs that respond appropriately to questions, commands, and statements expressed in human language, particularly English. Role of knowledge representation and linguistic theory. Students become familiar with current literature to implement a limited natural language processor.

681 Designing Expert Systems (3:3:0) Prerequisite: CS 580. Design, construction, and evaluation of software systems that solve problems generally deemed to require human expertise. Focuses on study and use of relevant languages, environments, mathematics, and logic. Case studies of successful systems. Programming projects include development of tools or small-scale systems.

682 Computer Vision (3:3:0) Prerequisite: CS 580 and 583. Study of computational models of visual perception and their implementation in computer systems. Topics include early visual processing, edge detection, segmentation, intrinsic images, image modeling, representation of visual knowledge, and image understanding.

683 Parallel Algorithms (3:3:0) Prerequisite: CS 583; CS 635 recommended. Examines design and analysis of parallel algorithms. Material focuses on algorithms for both theoretical and practical models of parallel computation. Considers algorithm design and analysis for PRAM and existing SIMD and MIMD type architectures. Topics include sorting, graph algorithms, numerical algorithms, and computational complexity.

684 Graph Algorithms (3:3:0) Prerequisite: CS 583. Data structures and analytical techniques for the study of graph algorithms. Data structures include disjoint sets, heaps, and dynamic trees. Algorithms include minimum spanning trees, shortest path, maximum flow, and graph planarity.

685/ECE 651/SYST 672 Intelligent Systems for Robots (3:3:0)Prerequisite: CS 580; or ECE 650; or SYST 611 or 555; or equivalent. Review of developments in intelligent autonomous systems. Studies applications of artificial intelligence, computer vision, and machine learning to robotics. Topics include analysis and design of algorithms and architectures for planning, navigation, sensory data understanding, sensor fusion, spatial reasoning, motion control, knowledge acquisition, learning of concepts and procedures, self-organization, and adaptation to the environment.

686 Image Processing and Applications (3:3:0) Prerequisites: CS 583 and either STAT 344 or MATH 351, or equivalent. Concepts and techniques used in image processing. Discusses methods for image capture, transformation, enhancement, restoration, and encoding. Students complete projects involving naturally occurring images.

687 Advanced Artificial Intelligence (3:3:0) Prerequisite: CS 580. Explores foundational issues of artificial intelligence, such as the roles of knowledge and search, formalization of knowledge and inference, and symbolic versus emergent approaches to intelligence. Studies advanced programming techniques for artificial intelligence and their relationship to foundational issues and most important application areas for artificial intelligence. Major programming project required.

688 Neural Network Principles (3:3:0) Prerequisite: CS 580 or equivalent. Study of neural network models, algorithms, and applications. Introduces several connectionist and biologically based models, and discusses their capabilities and limitations. Presents variety of application areas. Network simulation project is required.

697 Independent Reading and Research (1-3:0:0)Prerequisites: graduate standing, completion of at least two core courses (CS 540, 571, 580, 583), and permission of instructor. In areas of importance but insufficient demand to justify a regular course, students may undertake a course of study under supervision of consenting faculty member. Students usually submit written statement of course content, and tentative reading list as part of request for approval. Literature review, project report, or other written product usually required.

699 Advanced Topics in Computer Science (3:3:0) Prerequisites: completion of two core courses and permission of instructor. Special topics in computer science not occurring in regular computer science sequence. May be repeated for credit when subject distinctly different.

700 Quantitative Methods and Experimental Design in Computer Science (3:0:0) Prerequisites: STAT 344, at least two 600 level courses in computer science, and doctoral status. Integrated treatment to models and practices of experimental computer science. Topics include scientific methods applied to computing, workload characterization, forecasting of performance and quality metrics of systems, uses of analytic and simulation models, design of experiments, interpretation and presentation of experimental results, hypothesis testing, and statistical analyses of data. Involves one or more large-scale projects.

706 Concurrent Software Systems (3:3:0) Prerequisites: CS 571 and SWE 621 or 631 or equivalent. Study of issues related to development of concurrent software systems. Topics include concurrent programming languages and constructs; and specification, design, verification, and validation of concurrent programs. Students required to solve concurrent programming problems and check their solutions by using verification, testing, and debugging tools.

707 Distributed Software Systems (3:3:0) Prerequisite: CS 706 or permission of instructor. Issues in design and implementation of distributed applications. Topics include distributed programming using sockets as well as higher-level technologies such as remote procedure calls and distributed object middleware technologies including Java RMI, CORBA, and DCOM.

735 Concurrency (3:3:0) Prerequisite: CS 635, 706, or equivalent. Study of techniques and tools for specifying and verifying concurrent and distributed programs. Topics may include model checking, temporal logic, process algebra, and test generation. Automated verification tools used to specify and verify concurrent programs.

750/IT 750 Theory and Applications of Data Mining (3:3:0) Prerequisite: CS 450 and 580 or permission of the instructor. Concepts and techniques in data mining and their multidisciplinary applications. Topics include databases; data cleaning and transformation; concept description; association and correlation rules; data classification and predictive modeling; performance analysis and scalability; data mining in advanced database systems, including text, audio, and images; and emerging themes and future challenges. Term project and topical review required.

752 Interactive Graphics Software (3:3:0) Prerequisite: CS 652. Advanced graphics methods and tools. Topics include visualization, modeling, rendering, animation, simulation, virtual reality, graphics software tools, and current research topics.

755 Advanced Computer Networks (3:3:0) Prerequisite: CS 656. Current and emerging issues in advanced computer networks and applications. Topics include software systems associated with packet and cell-switched networking architectures and protocols, high-performance LANs, scheduling and congestion control, mobile networking, multimedia applications, and next generation of Internet.

756 Performance Analysis of Computer Networks (3:3:0) Prerequisite: CS 656 or equivalent. Analytical and simulation techniques for modeling and analysis of computer networks. Examines elementary queuing analysis; networks of queues; routing and flow controls; and applications to local and wide area networks, Internet, and emerging networking technologies.

773 Real-Time Systems Design and Development (3:3:0) Prerequisite: CS 656 or 671. Real-time systems and principles supporting design and implementation. Emphasis on fundamental results from real-time scheduling theory, and relevance to computer system design. Topics include system design issues for real-time applications involving communication networks, operating systems, databases, and multimedia.

775/ECE 749/IT 844 Pattern Recognition (3:3:0) Covers statistical pattern recognition, neural network, and statistical learning theory approaches. Topics include decision theory and BayesÕ theorem, density (parametric and nonparametric) estimation, linear and nonlinear discriminant analysis, SVM and kernel methods, SRM and model selection, performance evaluation, mixture of experts (AdaBoost), dimensionality reduction, feature selection and extraction, and clustering. Experimental design, applications, and performance evaluation emphasized.

777 Human-Computer Intelligent Interaction (3:3:0) Prerequisites: CS 580 and 652 or 682, or permission of the instructor. Current and emerging issues in human-computer intelligent interaction and human-centered systems and their applications. Topics include video processing, visualization, virtual environments, adaptation and tutoring, image and scene modeling, analysis and synthesis, face and gesture recognition, speech and natural language processing. Term project and topical review required.

780/IT 835 Computational Vision (3:3:0) Prerequisites: CS 682, 686, or permission of instructor. Study of recent advances in development of machine vision algorithms and knowledge-based vision systems. Topics include scalespace; Gabor and wavelet processing; distributed and hierarchical processing using neural networks; motion analysis; active, functional, and selective perception; object and target recognition; expert systems; data fusion; and machine learning. Emphasis on system integration in terms of perception, control, action, and adaptation. Presents applications to robotics, intelligent highways, inspection, forensic, and data compression.

782 Machine Learning (3:3:0) Prerequisite: CS 681, 687, or 688; or permission of instructor. Surveys machine learning concerning development of intelligent adaptive systems that are able to improve through learning from input data or from their own problem-solving experience. Topics provide broad coverage of developments in machine learning, including basic learning strategies and multistrategy learning.

785 Knowledge Acquisition and Problem Solving (3:3:0) Prerequisite: CS 680, 681, or 687; or permission of instructor. Principles and major methods of basic stages of knowledge acquisition (systematic elicitation of expert knowledge, knowledge base refinement, and knowledge base optimization) in the context of general problem-solving methods. Case studies of successful knowledge acquisition and problem solving systems. Projects include development or application of knowledge acquisition tools for knowledge-based systems.

798 Project Seminar (3:3:0) Prerequisite: 18 credits applicable toward MS in computer science. MasterÕs degree candidates undertake a project using the knowledge gained in the MS program. Topics chosen in consultation with advisor. Meets project or thesis requirement for the MS in computer science.

799 Thesis (3-6:0:0)Prerequisite: 18 credits applicable toward MS in computer science. Original or expository work evaluated by a committee of three faculty members.

803, 804 Doctoral Tutorial in Information Technology (3:3:0) Individualized intensive study of information technology. May be repeated as needed.

809/IT 809 Scaling Technologies for E-business (3:3:0) Prerequisites: at least one operating system and one networking course, and admission to an IT&E doctoral program. Discusses, from a quantitative point of view, characteristics of the most important technologies used to support the implementation of e-business sites. Includes topics such as hardware and software architectures of e-business sites, authentication, payment services, understanding customer behavior, workload characterization, scalability analysis, and performance prediction. Term paper and project required.

811/IT 811 Principles of Machine Learning and Inference (3:3:0) Prerequisite: CS 580, 681, or permission of instructor.Presents unifying principles that underlie diverse methods, paradigms, and approaches to machine earning and inference. Reviews the most known learning and inference systems, discusses strengths and limitations, and suggests most appropriate areas of their application. Hands-on experience by experimenting with state-of-the-art learning and inference systems, and working on projects tailored to research interests.

812/IT 812 Advanced Topics in Natural Language Processing (3:3:0) Prerequisite: CS 680. Advanced treatment of topics in syntax, semantics, and generation of linguistic output. Implementation and applications also discussed.

815/IT 815 Parallel Computation (3:3:0) Prerequisite: CS 635 or IT 816, or CSI 801. Topics illustrating some of the contemporary thinking on architectures, application, development environments, algorithms, operating system related issues, language requirements, and performance for parallel computation.

816/IT 816 Parallel Architectures, Algorithms, and Applications (3:3:0) Prerequisites: CS 583 and computer architecture course. Familiarization for students in area of parallel architectures, algorithms, and parallel computers. Discusses various algorithms and their applicability to certain architectures. Compares parallel algorithms with certain tools, and explores applications to artificial intelligence, image processing, and database machines.

817/IT 817 Neural Networks (3:3:0) Prerequisite: CS 688 or permission of instructor. Studies adaptive and competitive principles using distributed and parallel computation. Topics include background from statistics, control, adaptive signal processing, and neurosciences. Basic models, such as those suggested by Grossberg, Hopfield, and Kohonen, discussed in terms of analytical characteristics and applications. Neural networks assessed as universal approximators. Connections to fuzzy approach established through the Radial Basis Function approach. Presents applications to perception, knowledge-based systems, and robotics.

818/IT 818 Topics in High Performance Computer Systems (Scalable E-business Site Technologies and Models) (3:3:0) Studies technologies and architectures used to support e-business sites: multitier software and hardware architectures, security protocols, and payment protocols. Performance metrics for e-business. Performance impacts of e-business technologies. Performance models of e-commerce technologies. Workload characterization for e-business.

840/CS 685/ECE 750/SYST 672/IT 840 Intelligent Systems for Robots (3:3:0) Prerequisite: SYST 555, 611; ECE 650; CS 580; or equivalent. Reviews recent developments in intelligent autonomous systems. Studies applications of artificial intelligence, control theory, operations research, decision sciences, computer vision, and machine learning to robotics as well as correspondences between various fields. Topics include analysis and design of methods, and algorithms and architecture for planning, navigation, sensory data understanding, visual inspection, spatial reasoning, motion control, learning, self-organization, and adaptation to the environment.

852/IT 852 Graphical Real-Time Simulation (3:3:0) Prerequisite: CS 652 or IT 875. Current research in advanced computer graphics, and its applications in realistic real-time simulations. Topics include physically based modeling, real-time simulation, distributed interactive simulation (DIS), network virtual environments (NVE), and virtual reality (VR)

858/IT 858 Logic Models in Artificial Intelligence (3:3:0) Prerequisite: CS 580. Examines relevance of logic theory to artificial intelligence. Familiarizes students with variety of formal logics used in artificial intelligence, as well as ongoing research in new logics. Topics include first-order predicate calculus, resolution and non-resolution theorem proving, nonmonotonic logic, assumption-based reasoning, relationship between symbolic and quantitative theories of uncertainty, temporal logics, and their application to planning and metareasoning.

910/IT 910 Advanced Topics in Artificial Intelligence (3:3:0) Prerequisite: graduate course in artificial intelligence. Special topics in artificial intelligence not occurring in regular computer science sequence. Requires substantial student participation. Subject matter may include continuation of existing 600- or 700-level courses in artificial intelligence or other topics. May be repeated for credit when subject matter differs.

915/IT 915 Advanced Topics in Parallel Computation (3:3:0) Prerequisite: CS 815. Discussion of current research topics in parallel computation. Topics vary according to student and faculty interest. Possible topics include formal models of concurrency, specification and design of parallel programming languages, logic programming in a parallel environment, and parallel distributed processing (neural networks).

990/IT990 Dissertation Topic Presentation (1:0:0) Prerequisite: completion of all course requirements for PhD in IT, or permission of instructor. Opportunity for PhD students to present their research proposal for critique to interested faculty and students. Covers presentation of research topic for PhD in information technology; required of all PhD students. Students complete dissertation research proposal. May be repeated with a change in topic, although degree credit is given once.

998 Doctoral Dissertation Proposal (1-12:0:0) Work on a research proposal that forms basis for doctoral dissertation. May be repeated as needed. No more than 24 credits of CS 998 and 999 may be applied to doctoral degree requirements.

999 Doctoral Dissertation (1-12:0:0) Formal record of commitment to doctoral dissertation research under direction of faculty member in computer science. May be repeated as needed. No more than 24 credits of CS 998 and 999 may be applied to doctoral degree requirements.