WSU Graduate Courses - Computer Science/CS

CS 516 NUMERICAL METHODS FOR DIGITAL COMPUTERS I (Credits: 4)

(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 251 OR CEG 220;
MTH 231, 253 OR 235 OR 255.

CS 516 NUMERICAL METHODS FOR DIGITAL COMPUTERS LABORATORY (Credits: )

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.

CS 517 NUMERICAL METHODS FOR DIGITAL COMPUTERS II (Credits: 4)

PREREQUISITE: CS 516, MTH 233 OR 235, 253 OR 355.

CS 517 NUMERICAL METHODS FOR DIGITAL COMPUTERS LABORATORY (Credits: )

CS 600 DATA STRUCTURES AND SOFTWARE DESIGN (Credits: 4)

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, MTH 253, 257, CEG 333.

CS 600 DATA STRUCTURES LABORATORY (Credits: )

CS 605 INTRODUCTION TO DATA BASE MANAGEMENT SYSTEMS (Credits: 4)

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.

CS 605 CASE STUDIES IN INFORMATION SYSTEMS LABORATORY (Credits: )

CS 607 OPTIMIZATION TECHNIQUES (Credits: 3)

(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.

CS 609 PRINCIPLES OF ARTIFICIAL INTELLIGENCE (Credits: 4)

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).

CS 610 THEORETICAL FOUNDATIONS OF COMPUTING (Credits: 4)

(Also listed as MTH 610.) Turing machines; m-recursive functions; equivalence of computing paradigms; Church-Turing thesis; undecidability; intractability. 3 hours lecture, 2 hours lab.

PREREQUISITE: CS 666.

CS 610 THEORETICAL FOUNDATIONS OF COMPUTING LABORATORY (Credits: )

Turing machines; m-recursive functions; equivalence of computing paradigms; Church-Turing thesis; undecidability; intractability. 3 hours lecture, 2 hours lab.

CS 619 CRYPTOGRAPHY AND DATA SECURITY (Credits: 3)

(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.

CS 658 APPLIED GRAPH THEORY (Credits: 3)

(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.

CS 659 COMBINATORIAL TOOLS FOR COMPUTER SCIENCE (Credits: 3)

(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.

CS 666 INTRODUCTION TO FORMAL LANGUAGES (Credits: 4)

Introduction to the theory of formal languages and automata. Emphasis is on those classes of languages commonly encountered by computer scientists, such as regular and context-free languages. 3 hours lecture, 2 hours lab.

PREREQUISITE: MTH 257, CS 600; OR MTH 257 AND COMPLETION OF
A 600-LEVEL MATH OR STATISTICS COURSE.

CS 666 INTRODUCTION TO FORMAL LANGUAGES LABORATORY (Credits: )

CS 670 SYSTEMS SIMULATION (Credits: 4)

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 600 AND STT 560 OR STT 363.

CS 670 SYSTEMS SIMULATION LABORATORY (Credits: )

CS 671 ALGORITHMS FOR BIOINFORMATICS (Credits: 4)

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.

PREREQUISITE: CS 600, BIO 210, BIO 211, CHM 213 OR
PERMISSION OF INSTRUCTOR.

CS 680 COMPARATIVE LANGUAGES (Credits: 4)

Basic concepts and special purpose facilities in programming languages, examined through several representative languages. 3 hours lecture, 2 hours lab.

PREREQUISITE: CS 600.

CS 680 COMPARATIVE LANGUAGES LABORATORY (Credits: )

Basic concepts and special purpose facilities in programming languages, examined through several representative languages. 3 hours lecture, 2 hours lab.

CS 682 SCANNING, PARSING, AND SEMANTIC ANALYSIS (Credits: 4)

Study and use of tools for performing lexical, syntactic, and semantic analysis of computer-oriented languages.

PREREQUISITE: CS 666, CS 680.

CS 699 SELECTED TOPICS (Credits: 1 TO 5)

Study of selected topics in computer science. Titles vary. May be taken for a letter grade or pass/unsatisfactory.

CS 700 PRIN INSTR COMPUTER SCI (Credits: 3)

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.

CS 701 DATABASE SYSTEMS AND DESIGN (Credits: 4)

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.

CS 701 DATABASE SYSTEMS AND DESIGN LABORATORY (Credits: )

CS 705 DATA MINING (Credits: 4)

Data forms, data preparation, cleaning, feature selection, diseretization, high-level statistical analysis; associations; calssification; clustering, data cubes; interestingness, cross-validation; visualization; scalability; privacy and ethics; applications. 3 hours lecture, 2 hours lab.

PREREQUISITE: CS 605

CS 711 KNOWLEDGE-BASED SYSTEMS IN ARTIFICIAL INTELLIGENCE (Credits: 4)

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).

CS 711 ARTIFICIAL INTELLIGENCE II LABORATORY (Credits: )

CS 712 ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE (Credits: 4)

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.

CS 712 ARTIFICIAL INTELLIGENCE III LABORATORY (Credits: )

CS 714 MACHINE LEARNING I (Credits: 4)

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.

CS 716 NUMERICAL ANALYSIS I: APPLIED LINEAR ALGEBRA (Credits: 4)

(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).

CS 717 NUMERICAL ANALYSIS II: FINITE DIFFERENCE METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS (Credits: 4)

(Also listed as MTH 717.) Finite difference methods for partial differential equations; analysis of stability and convergence.

PREREQUISITE: CS 716, MTH 333, 431.

CS 718 NUMERICAL ANALYSIS III: FINITE ELEMENT METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS (Credits: 4)

(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.

CS 735 EVALUATION AND PREDICTION OF SYSTEM PERFORMANCE (Credits: 4)

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: CEG 633, CS 670.

CS 735 EVALUATION AND PREDICTION OF SYSTEM PERFORMANCE LABORATORY (Credits: )

CS 740 COMPUTATIONAL COMPLEXITY AND ALGORITHM ANALYSIS (Credits: 4)

Time complexity analysis of algorithms; computational complexity; NP completeness. 3 hours lecture, 2 hours lab.

PREREQUISITE: CS 610, CS 666.

CS 740 INTRODUCTION TO THE THEORY AND ANALYSIS OF ALGORITHMS LABORATORY (Credits: )

Time complexity analysis of algorithms; computational complexity; NP completeness. 3 hours lecture, 2 hours lab.

CS 765 FOUNDATIONS OF NEUROCOMPUTING (Credits: 4)

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, MTH 253, CS 600.

CS 766 EVOLUTIONARY COMPUTING (Credits: 4)

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.

CS 767 FUZZY SET THEORY AND APPROXIMATE REASONING (Credits: 4)

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 processing.

PREREQUISITE: CS 600.

CS 771 NATURAL LANGUAGE TECHNIQUES (Credits: 4)

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).

CS 772 ADVANCED NATURAL LANGUAGE PROCESSING CONCEPTS (Credits: 4)

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.

CS 774 LOGIC PROGRAMMING (Credits: 4)

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.

CS 776 FUNCTIONAL PROGRAMMING (Credits: 4)

In-depth look at functional programming techniques, and functional languages and their implementation.

PREREQUISITE: CS 680.

CS 780 COMPILER DESIGN AND CONSTRUCTION (Credits: 4)

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.

CS 780 COMPILER DESIGN AND CONSTRUCTION LABORATORY (Credits: )

Complete compiler for a small programming language is discussed. Topics covered are scanning, syntax analysis, and code generation. 3 hours lecture, 2 hours lab.

CS 781 COMPILER DESIGN AND CONSTRUCTION II (Credits: 4)

Continuation of CS 780. Topics are covered in more depth. Project is required. 3 hours lecture, 2 hours lab.

PREREQUISITE: CS 780.

CS 781 COMPILER DESIGN AND CONSTRUCTION II LABORATORY (Credits: )

Continuation of CS 780. Topics are covered in more depth. Project is required. 3 hours lecture, 2 hours lab.

CS 782 COMPILER DESIGN AND CONSTRUCTION III (Credits: 4)

Continuation of CS 781. Concentration on major design project. 3 hours lecture, 2 hours lab.

PREREQUISITE: CS 781.

CS 782 COMPILER DESIGN AND CONSTRUCTION III LABORATORY (Credits: )

Continuation of CS 781. Concentration on major design project. 3 hours lecture, 2 hours lab.

CS 784 PROGRAMMING LANGUAGES (Credits: 4)

Programming paradigms and concepts for high level programming languages. Techniques for formal specification.

PREREQUISITE: CS 680.

CS 790 SELECTED TOPICS IN COMPUTER SCIENCE (Credits: 4)

Lectures on and study of selected topics in current research and recent developments in computer science. 3 hours lecture, 2 hours lab.

CS 790 SELECTED TOPICS IN COMPUTER SCIENCE LABORATORY: (Credits: )

Lectures on and study of selected topics in current research and recent developments in computer science. 3 hours lecture, 2 hours lab.

CS 795 INDEPENDENT STUDY (Credits: 1 TO 4)

Special problems in advanced computer science topics. Graded pass/unsatisfactory.

CS 799 THESIS (Credits: 1 TO 8)

Graded pass/unsatisfactory.

CS 801 ADVANCED TOPICS IN DATABASE SYSTEMS (Credits: 4)

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.

CS 840 ADVANCED TOPICS IN THE THEORY OF COMPUTATION (Credits: 4)

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.

CS 865 ADVANCED TOPICS IN SOFT COMPUTING (Credits: 4)

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.

PREREQUISITE: CS 765 OR CS 766 OR CS 767.

CS 884 ADVANCED TOPICS IN PROGRAMMING LANGUAGES (Credits: 4)

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

CS 890 SELECTED TOPICS (Credits: 1 TO 4)

Selected topics in computer science and engineering.

CS 891 PH D SEMINAR (Credits: 1)

Registration in the Ph.D. seminar is required of all students seeking the Ph.D. in computer science and engineering. Graded pass/unsatisfactory.

CS 892 PHD QUALIFYING EXAM (Credits: 1 TO 8)

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.

CS 894 CANDIDACY EXAM (Credits: 1)

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.

CS 895 INDEPENDENT STUDY (Credits: 1 TO 8)

Independent study in a chosen area for Ph.D. research. Graded pass/unsatisfactory.

CS 896 DISSERTATION DEFENSE (Credits: 1)

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.

CS 897 RESIDENCY RESEARCH (Credits: 1 TO 12)

Research on the Ph.D. dissertation topic taken in residence. Graded pass/unsatisfactory.

CS 898 DISSERTATION RESEARCH (Credits: 1 TO 12)

Research on the Ph.D. dissertation topic not taken in residence. Graded pass/unsatisfactory.