Computer Science (CS)

Computer Science

105 Computer Ethics and Society (1:1:0). Prerequisite: 12 hours of undergraduate course work. Intensive introduction to the legal, social, and ethical issues surrounding software development and computer use. Professional conduct, social responsibility, and rigorous standards for software testing and reliability are stressed. Issues such as liability, ownership of information, privacy, security, and crime are examined. Students read, write, discuss, and present reports on these topics. Students who have received credit for CS 305 cannot apply credit for this course towards the major requirements of the B.S. in Computer Science.

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 a serious interest in computer science. Topics include an 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 the major topics of this course.

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: Junior standing (at least 60 credit hours). A practical course intended to assist students in becoming effective computer professionals by examining the challenging legal and ethical issues surrounding computer technology and its use, and building a foundation to deal with those challenges. Applies philosophical bases for ethical decision-making to modern concerns raised by computers and technology. Addresses the specific topics covered by CS 105 in a more intensive manner, and in addition, focuses on the emerging legal and ethical issues involved in e-commerce and the widespread use of the Internet.

306 Synthesis of Ethics and Law for the Computing Professional (3:3:0). Prerequisties: Computer Science majors who have completed at least 60 credits and who have completed or are concurrently enrolled in all required general education courses. For a 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. The 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 265. 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 CS 265 and ECE 301. Computer hardware organization, software structure, and data organization. Students complete a term project that simulates one computer system on another.

391 Advanced Programming Lab (1:0:1). Corequisite: Grade of C or better in CS 310 and permission of instructor. In this programming intensive lab course, students refine their problem solving and programming skills, while gaining experience in teamwork. The material focuses on data structures, recursion, backtracking, dynamic programming, and debugging. The central focus is on the application of both 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 a 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, compilers, and CASE tools. 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 sublanguages 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 in the context of applications.

451 Computer Graphics and Software Design (3:3:0). Prerequisites: Grade of C or better in MATH 203, 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, 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 the 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. The course 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 ( e.g., client-server programming using sockets and remote procedure calls).

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 both MATH 203 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). Formerly CS 465. Prerequisites: Grade of C or better in CS 310, 330, and MATH 114. Analysis of the computational resources required for important problem types by alternative algorithms and their associated data structures, using mathematically rigorous techniques. Specific algorithms are 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 the design and successful implementation of a major software project, encompassing a broad spectrum of knowledge and skills, developed by a team of students. Final exhibition of the result to a faculty/industry panel is required. f,s

491 Great Principles of Information Technology (3:3:0). Prerequisites: senior standing (at least 90 credit hours) including two 400-level CS courses. A synthesis course for CS majors. Offers a holistic view of the field and its connections with other fields. Covers great principles of information technology from algorithms and programming, distributed systems, and cooperative systems. Emphasizes the historical development of these principles, why they have stood the tests of time, how they relate to one another, and how they relate to issues in other fields. Also covers major contemporary open questions in information technology. Includes a project with an oral presentation.

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 the department chair before registering. May be repeated for a maximum of six credits if the 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 six credits if the 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. LISP, PROLOG, or an expert system programming language is used.

583 Analysis of Algorithms (3:3:0). Prerequisites: CS 310, 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), the 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 a graduate course in object-oriented programming or equivalent. Principles of object-oriented design through design patterns. A study of the selection of appropriate object-oriented structure after the system requirements or requirements specification of the software system have been developed. Design patterns are created in the logic view of the software system. A study of generalized design solutions for generalized software design problems. A study of the 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. Emphasis is placed on the interfaces between algorithm design and implementation, architecture, and software. Parallel algorithms and parallel programming languages are examined relative to the architecture of particular parallel computers.

640 Advanced Compilers (3:3:0). Prerequisites: CS 540 and 583 or equivalent. Examination of advanced compiler techniques such as code optimizations for sequential and parallel machines; compilers for logical, functional, or object oriented languages; and other selected topics in the 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 the layers of the 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. Emphasis is on the TCP/IP family of protocols.

668 Computer Architecture Systems (3:3:0). Prerequisite: CS 571 or 540 or equivalent. Examination of the principles and practices relating computer architecture to programming execution and efficiency. A new approach that stresses the performance and cost of architecture is presented. The principles of compiler and OS implications, instructions sets, basic processors, pipelines, and memory-hierarchy are examined. Specific 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 the design and implementation of microkernel-based, object-oriented, and distributed operating systems. Specific topics include support for interprocess communication, the 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 involved 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 the principles of the design of 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 the study and use of relevant languages, environments, mathematics, and logic. Case studies of successful systems are examined. 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 on 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. Examination of the design and analysis of parallel algorithms. Material focuses on algorithms for both theoretical and practical models of parallel computation. Algorithm design and analysis for the PRAM are considered, as well as for 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 discussed include disjoint sets, heaps, and dynamic trees. Algorithms treated 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 recent developments in the area of intelligent autonomous systems. Applications of artificial intelligence, computer vision, and machine learning to robotics are studied. 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 equiva lent. Concepts and techniques used in image processing. Methods for image capture, transformation, enhancement, restoration, and encoding are discussed. Students complete projects involving naturally occurring images.

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

688 Neural Network Principles (3:3:0). Prerequisite: CS 580 or equivalent. Study of neural network models, algorithms, and applications. Several connectionist and biologically based models are introduced, and their capabilities and limitations are discussed. Variety of application areas are presented. 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, a student may undertake a course of study under the supervision of a consenting faculty member. Students normally submit a written statement of the content of the course and a tentative reading list as part of the request for approval to take the course. Literature review, project report, or other written product is normally required.

699 Advanced Topics in Computer Science (3:3:0). Prerequisites: Completion of at least two core courses and permission of instructor. Special topics in computer science not occurring in the regular computer science sequence. May be repeated for credit when the subject is 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 the 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 the development of concurrent software systems. Topics include concurrent programming languages and constructs and the specification, design, verification, and validation of concurrent programs. Students are required to solve concurrent programming problems and to 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 the design and implementation of distributed applications. Topics covered 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. Potential topics include model checking, temporal logic, process algebra, and test generation. Automated verification tools will be used to specify and verify concurrent programs.

750/IT 750 Theory and Applications of Data Mining (3:3:0). Prerequisite: CS 681, 687, or 688, 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 their 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 the next generation of the 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, internets, and emerging networking technologies such as Asynchronous Transfer Mode.

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

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.

782 Machine Learning (3:3:0). Prerequisite: CS 681, 687, or 688 or permission of instructor. Survey of the field of machine learning that is concerned with developing 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 past and current 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 the 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 are presented. Projects include development or application of knowledge acquisition tools for knowledge-based systems.

798 Project Seminar (3:3:0). Prerequisite:18 credits applicable toward the M.S. in Computer Science. Master's degree candidates undertake a project using the knowledge gained in the M.S. program. Topics are chosen in consultation with an advisor. Intended to meet the project or thesis requirement for the M.S. in Computer Science.

799 Thesis (1-6:0:0). Prerequisite:18 credits applicable toward the M.S. in Computer Science. Original or expository work evaluated by a committee of three faculty members.

998 Doctoral Dissertation Proposal (1-12:0:0). Work on a research proposal that forms the basis for a 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). A formal record of commitment to doctoral dissertation research under the direction of a 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.