516-4, Numerical Methods for Digital Computers
(Also listed as MTH 516, 517.) Introduction to numerical methods used in the sciences. Includes methods of interpolation, data smoothing, functional approximation, integration, solutions of systems of equations, and solutions of ordinary differential equations. 3 hours lecture, 2 hours lab. Prerequisite: CS 142 or EGR 153 or CS 241; MTH 231, 253 or 255
517-4 Numerical Methods for Digital Computer
(Also listed as MTH 516, 5170 Introduction to numerical methods used in the science. Includes methods of interpolation, data smoothing, functional approximation, integration, solutions of systems of equations, and solutions of ordinary differential equations. 3 hours lecture, 2 hours lab. Prerequisite: CS 516, MTH 233, 253, or 355.
550-4 Computational Tools and Techniques for Data Analysis
Introduction to the representation, visualization, and modeling of large data sets. Data Analysis using standard high level software tools. Topics include data filtering, clustering, classification, and data mining. Prerequisite: None
600-4 Data Structures and Algorithms
Study of the implementation of data structures and control structures in professional computer programs. Introduction to the fundamentals of complexity and analysis. Study of common standard problems and solutions (e.g., transitive closure and critical paths). Emphasis is on high-level language software design. 3 hours lecture, 2 hours lab. Prerequisite: CS 242 and CEG 233
605-4 Introduction to Database Management Systems
Survey of logical and physical aspects of database management systems, including entity-relationship and relational data models; physical implementation methods; query languages; SQL, relational algebra, relational calculus, and QBE; experience in creating and manipulating databases. Prerequisite: CS 600.
607-3 Optimization Techniques
(Also listed as MTH 607.) Concepts of minima and maxima; linear programming; simplex method; densitivity, and duality; transportation
and assignment problems, dynamic programming. Prerequisite: MTH 233 and MTH 253 or 255.
609-4 Principles of Artificial Intelligence
Problem-solving methods in artificial intelligence (AI) with emphasis on heuristic approaches. Topics
include knowledge representation, search, intelligent agents, planning, learning, natural language processing, logic, inference, robotics, and case-based reasoning. 3 hours lecture, 2 hours lab. Prerequisite: CS 600, (CS 340-LISP or LISP programming experience).
610-4 Theoretical Foundations of Computing
(Also listed as MTH 610.) Turing machines; partial-recursive functions; equivalence of computing paradigms; Church-Turing thesis; undecidability; intractability. Prerequisite:
CS 666.
619-3 Cryptography and Data Security
(Also listed as MTH 619.) Introduction to the mathematical principles of data security. Various developments in cryptography are discussed, including public-key encryption, digital signatures, the data encryption standard (DES), key safeguarding schemes. Prerequisite: MTH 253 or 255.
658-3 Applied Graph Theory
(Also listed as MTH 658.) Introduction to methods, results, and algorithms from graph theory. Emphasis on graphs as mathematical models applicable to organizational and industrial situations. Prerequisite: CS 142 or 241, MTH 231.
659-3 Combinatorial Tools for Computer Science
(Also listed as MTH 659.) Introduction to some of the mathematical tools needed for understanding computer programming. Topics include summations, elementary number theory, combinatorial identities, generating functions, and asymptotics. Prerequisite: MTH 280;
MTH 457 recommended.
666-4 Introduction to Formal Language
Introduction to the theory of formal languages and automata. Emphasis is on those classes of languages commonly encountered by computer scientists (e.g. regular and context-free languages). Prerequisite: CS 600, MTH 257; or MTH 257 and completion of a 600-level math or statistics course.
670-4 Systems Simulation
Introduction to simulation and comparison with other techniques; discrete simulation models; introduction to queuing theory and stochastic processes; comparison of simulation languages; simulation methodology; selected applications of simulation. Students must show ability to solve problems using simulation techniques. 3 hours lecture, 2 hours lab. Prerequisite: CS 400 or 600 and (STT 360 or STT 363 or ISE 301).
671- 4 Algorithms for Bio-informatics
Theory-oriented approach to the application of contemporary algorithms to bioinformatics. Graph Theory, complexity theory, dynamic programming and optimization techniques are introduced in the context of application toward solving specific computational problems in molecular genetics. Prerequisites: CS 600, BIO 210, BIO 211, CHM 213 or permission of instructor.
680-4 Comparative Languages
Basic concepts and special purpose facilities in programming languages, examined through several representative languages. 3 hours lecture, 2 hours lab. Prerequisite: CS 600.
682-4 Scanning, Parsing, and Semantic Analysis
Study and use of tools for performing lexical, syntactic, and semantic analysis of computer-oriented languages. Prerequisite: CS 666,
CS 680.
699-1 to 5 Selected Topics
Study of selected topics in computer science. Titles vary. May be taken for a letter grade or pass/unsatisfactory.
700-3 Principles of Instruction in Computer Science
A survey of available instructional materials and discussion of educational theory and techniques leading to more effective instruction. For graduate teaching assistants in the Department of Computer Science only.
701-4 Database Systems and Design
Introduction to basic goals and techniques in the design and implementation of information retrieval systems. Input, file organization, search strategies, output, language design, and evaluation techniques are covered. 3 hours lecture, 2 hours lab. Prerequisite: CS 605.
705-4 Data Mining
Data forms, data preparation, cleaning, feature selection, diseretization; high-level statistical analysis; associations; classification; clustering; data cubes; interestingness, cross validation; visualization; scalability; privacy and ethics; applications. 3 hours lecture, 2 hours lab. Prerequisite: CS 605.
707-4 Information Retrieval
This course covers models for information retrieval, techniques for indexing and searching, algorithms for classification and clustering, latent semantic indexing, link analysis and ranking.
711-4 Knowledge-Based Systems in Artificial Intelligence
Continuation of CS 609. Topics covered include techniques for handling judgmental knowledge, semantic networks, and frame-based systems. Useful constructs and architectures for AI systems are discussed. 3 hours lecture, 2 hours lab. Prerequisite: CS 609, (CS 340-LISP or LISP programming experience).
712-4 Advanced Topics in Artificial Intelligence
Covers advanced topics in artificial intelligence theory and applications. These are taken from such areas as natural language processing, machine learning, advanced AI programming techniques, and search and planning. Prerequisite: CS 609.
714-4 Machine Learning I
Reviews the development of machine learning paradigms. Introductory topics include parameter adjustment methods, signature tables, and the application of genetic algorithms to artificial intelligence problem domains. Prerequisite: CS 609
716-4 Numerical Analysis I: Applied Linear Algebra
(Also listed as MTH 716.) Topics chosen with emphasis on computational linear algebra. Systems of linear equations and Gaussian elimination; computation of eigenvalues and eigenvectors; matrix exponential; norm and condition number; and iterative methods. Prerequisite: CS 142,
MTH 355 (or knowledge of a higher-level language).
717-4 Numerical Analysis II: Finite Difference Methods for Partial Differential Equations
(Also listed as MTH 717.) Finite difference methods for partial differential equations; analysis of stability and convergence. Prerequisite: CS 716, MTH 333, 431.
718-4 Numerical Analysis III: Finite Element Methods for Partial Differential Equations
(Also listed as MTH 718.) Finite element methods for elliptic boundary value problems; analysis of errors; approximation by finite element spaces; effects of curved boundaries, numerical integration; finite element methods for parabolic problems. Prerequisite: CS 716, MTH 333, 431.
735-4 Evaluation and Prediction of System
Performance
Introduction to the modeling and analysis of computer system performance as a function of the hardware and software components of the system. 3 hours lecture, 2 hours lab. Completion of a statistics course required. Prerequisite: CS 670, CEG 633.
740-4 Computational Complexity and Algorithm Analysis
Time complexity analysis of algorithms; computational complexity; NP completeness. 3 hours lecture, 2 hours lab. Prerequisite: CS 610, CS 666.
756-4
765-4 Foundations of Neurocomputing
Information processing in neural networks as a mode of computation complementary to symbolic artificial intelligence, emphasizing common ideas across different network architectures. Current applications in machine learning and spatiotemporal pattern recognition will be evaluated. Prerequisite: MTH 232, 253, CS 600. RECOMMENDED: CS 710
766-4 Evolutionary Computing
This course explores evolutionary computation from a historical, theoretical, and an application viewpoint. Evolutionary search techniques including genetic algorithms, evolutionary programming, and genetic programming applied to problems in control, optimization, and classification are presented. Prerequisite: CS 600.
767-4 Fuzzy Set Theoryand Approximate Reasoning
Provides an introduction to fuzzy set theory that serves as a basis for the study of fuzzy rule-based systems, pattern classification, function approximation, modeling, and information process. Prerequisite: CS 600.
771-4 Natural Language Processing Techniques
Survey of issues that arise in computer understanding of natural languages like English. Topics include significance of language structure in extracting meaning, ambiguities, parsing techniques and case studies.
Prerequisite: CS 666, (LISP or CS 680).
772-4 Advanced Natural Language Processing Concepts
Continuation of CS 771. Computational methods for dealing with natural language semantics are introduced. Topics include semantic networks, conceptual dependency graphs, and formal logic as a semantic model. Prerequisite: CS 771.
774-4 Logic Programming
Theory and practice of logic programming. Application of Prolog to artificial intelligence, language analysis, and symbolic programming. Some attention to implementation issues, constraint logic programming, and concurrent logic languages. An acquaintance with Prolog is assumed. Prerequisite: CS 680 or CS 784.
776-4 Functional Programming
In-depth look at functional programming techniques, and functional languages and their implementation. Prerequisite: CS 680.
780-4 Compiler Design and Construction
Complete compiler for a small programming language is discussed. Topics covered are scanning, syntax analysis, and code generation. 3 hours lecture, 2 hours lab. Prerequisite: CS 666, CS 680.
781-4 Compiler Design and Construction II
Continuation of CS 780. Topics are covered in more depth. Project is required. 3 hours lecture, 2 hours lab. Prerequisite: CS 780.
782-4 Compiler Design and Construction III
Continuation of CS 781. Concentration on major design project. 3 hours lecture, 2 hours lab. Prerequisite: CS 781.
784-4 Programming Languages
Programming paradigms and concepts for high level programming languages. Techniques for formal specification. Prerequisite: CS 680.
790-4 Selected Topics in Computer Science
Lectures on and study of selected topics in current research and recent developments in computer science. 3 hours lecture, 2 hours lab.
795-1 to 4 Independent Study
Special problems in advanced computer science topics. Graded pass/unsatisfactory.
799-1 to 8 Thesis Research
Graded pass/unsatisfactory.
801-4 Advanced Topics in Database Systems
Continuation of CS 701 with emphasis on relational databases and distributed systems. Current literature will be reviewed. At least one programming project bridging the gap from theory to practice. Prerequisite: CS 701.
805-4 Advanced Data Mining
This advanced data mining course covers concepts and techniques on sequence mining, text mining, graph mining, data cube mining, microarray gene expression mining, stream mining, time series mining, web mining, bioinformatics, privacy issues, etc.
840-4 Advanced Topics in the Theory of Computation
Continuation of CS 610, 666, and 740. Covers advanced topics taken from formal language theory, predicate calculus, algorithm analysis, and complexity theory. 3 hours lecture, 2 hours lab. Prerequisite: CS 666 or CS 610 or CS 740.
865-4 Advanced Topics in Soft Computing
Covers advanced topics in soft computing. Soft computing paradigms include fuzzy set theory, neural networks evolutionary computing, and probabilistic and statistical techniques. Particularly, relationships and interactions between these disciplines will be explored. Prerequisites: CS 765 or CS 766 or CS 767.
875-4 Semantic Web
Semantic web extends current web using research in fields such as knowledge representation, AI, and database. Data is made meaningful and machine processable, leading to next generation of search, integration, and analysis.
884-4 Advanced Topics in Programming Languages
Continuation of CS 784. Emphasis on formal methods for specifying and defining both the syntax and the semantics of programming languages. Prerequisite: CS 784 or CS 780.
890-1 to 4 Selected Topics
Selected topics in computer science and engineering.
891-1 Ph.D. Seminar
Registration in the Ph.D. seminar is required of all students seeking the Ph.D. in computer science and engineering.
Graded pass/unsatisfactory.
892-1 to 8 Ph.D. Qualifying Exam
Examination that tests understanding of the fundamentals necessary to begin concentrated study in chosen Ph.D. research area. Composed of written tests and an oral exam. Must be passed within two attempts. Graded pass/unsatisfactory.
894-1 Candidacy Exam
Examination that tests for depth of understanding in a chosen computer science and computer engineering research area. Includes a written proposal for a Ph.D. topic and an oral examination, that is open to the public. Graded pass/unsatisfactory.
895-1 to 8 Independent Study
Independent study in a chosen area for Ph.D. research. Graded pass/unsatisfactory.
896-1 Dissertation Defense
Examination on the Ph.D. dissertation. The written dissertation is submitted and must be successfully defended in the oral exam conducted by the dissertation committee. Graded pass/unsatisfactory.
897-1 to 12 Residency Research
Research on the Ph.D. dissertation topic taken in residence. Graded pass/unsatisfactory.
898-1 to 12 Dissertation Research
Research on the Ph.D. dissertation topic not taken in residence. Graded pass/unsatisfactory
