The curriculum leading up to the baccalaureate degree in computer science is designed to prepare students for positions as computer scientists with business, industry, or government, or for graduate study in computer science. The main objectives are to impart to students an understanding of the basics of computer science, to develop proficiency in the practice of computing, and to prepare them for continued professional development.

The following are intended learning outcomes for computer science majors. Seniors will assess these outcomes in a survey conducted before they graduate and feedback thus obtained will be used to improve the curriculum.

A. Impart an understanding of the basics of the discipline

Each graduate will know

B. Develop proficiency in the practice of computing

The graduated student will be able to

C. Prepare for continued professional development

Students must earn at least a C- in each course taken to fulfill the Degree Program.

Students must take at least 45 credits at the 300 level or higher at Iowa State University.

To complete an undergraduate degree in Computer Science, a student must satisfy the requirements of the College of Liberal Arts and Sciences (see Liberal Arts and Sciences, Curriculum) and include the following courses within the group requirements: Phil 343; Sp Cm 212; 14 credits of math and statistics including Math 165, Math 166, one statistics course from Stat 105, 231, 305, 330, 333, or 341, and at least one math course from Math 265, 266, 304, 307, 314, or 317; a minimum of 13 credits of natural science including Phys 221, 222, and at least one additional natural science course from the following list: A Ecl 312, Anthr 202, 307, BBMB 221, Biol 312, Biol 355, Chem 163-231, Ent 370, Env S 324, Env S 330, FS HN 167, Gen 260, Geol 100-108, 201, 311, 451, 475, Mat E 207, 211, Mteor 206, 301, Psych 310. Communication Proficiency requirement: Engl 150, 250 and one of Engl 302, 305, 309 or 314. The minimum grade accepted in each of the three required English courses is a C-.

Students wishing to pursue the B.S. degree in computer science must first successfully complete the premajor program consisting of the following courses and minimum grade requirements:

Students majoring in computer science must successfully complete this premajor program prior to taking any other courses in the Department. Thus, for computer science majors, this premajor serves as a necessary prerequisite to all the other courses offered by the Department.

Computer science majors transferring from other institutions must take at least 15 of their credits at the 300-level or above in our department while in residence at Iowa State.

To graduate with a major in the Computer Science Department, a student must earn at least a C- in each of the courses taken to fulfill the program of study.

A minimum of 44 credits is required for the B.S. degree in computer science. The required courses are: Com S 101, 203, Cpr E 210, Com S 227, 228, 229, 309, 311, 321, 330, 331, 342, 352, 362 or 363. In addition, two advanced-level courses must be selected from the following groups:

Courses in Group W require written reports and those in Group B require both oral and written reports. Students must take one course from Group B and one course from any group.

Students must earn a C- or better in each course in the department which is a prerequisite to a course listed in the student's degree program.

Undergraduate Minor. The Computer Science Department offers an undergraduate minor in Computer Science. The minor requires at least 19 credit hours in computer science courses. Com S 227, 228, and 229, adding up to 10 credit hours are required. In addition, at least 9 credits should be taken in courses at the 300 level or above.

Undergraduate Curriculum in Software Engineering. The Department of Computer Science together with the Department of Electrical and Computer Engineering also offer a curriculum leading to an undergraduate degree in software engineering. The software engineering curriculum offers emphasis areas in software engineering principles, process, and practice. Students may also take elective courses in computer engineering and computer science.

See Index, Software Engineering. For curriculum information, see also College of Engineering and College of Liberal Arts and Sciences.

The department offers work for the degrees Master of Science and Doctor of Philosophy with a major in Computer Science. The Doctor of Philosophy degree may also be earned with computer science as a co-major with some other discipline. Additionally, the department offers a minor to students majoring in other departments.

Established research areas include algorithms, artificial intelligence, computational complexity, computer architecture, bioinformatics, computational biology, computer networks, database systems, formal methods, information assurance, machine learning and neural networks, multimedia, operating systems, parallel and distributed computing, programming languages, robotics, and software engineering. There are also numerous opportunities for interdisciplinary research.

Typically, students beginning graduate work in Computer Science have completed a bachelor's degree or equivalent in Computer Science. However, some students with undergraduate majors in other areas, such as mathematical, physical, or biological science or engineering become successful graduate students in Computer Science.

For the degree Master of Science, a minimum of 30 semester credits is required. A thesis demonstrating research and the ability to organize and express significant ideas in computer science is required.

The purpose of the doctoral program is to train students to do original research in Computer Science. Each student is also required to attain knowledge and proficiency commensurate with a leadership role in the field. The Ph.D. requirements are governed by the student's program of study committee within established guidelines of the department and the graduate college. They include coursework, demonstrated proficiency in four areas of Computer Science, a research skills requirement, a preliminary examination, and a doctoral dissertation and final oral examination.

The department recommends that all graduate students majoring in Computer Science teach as part of their training for an advanced degree.

Courses open for nonmajor graduate credit: 309, 311, 321, 330, 331, 342, 352, 362, 363, 381, 401, 416, 417, 418, 425, 426, 430, 440, 454, 455, 461, 471, 472, 474, 477, 481, 484.

** Com S 401. Projects in Computing and Business Applications. ** (2-2) Cr. 3. F. * Prereq: Engl 250, Sp Cm 212, Com S 309, and either 362 or 363. * Applications of software development methods (requirements collection and analysis, software design, project management, documentation and testing), programming techniques, database designs and administration, network application programming to solve computing needs in business settings. A study of practical applications of emerging technologies in computing. Emphasis on semester-long team programming projects. Lab assignments. Oral and written reports. Nonmajor graduate credit.

** Com S 409. Software Requirements Engineering. ** (Dual-listed with 509). (Cross-listed with S E). (3-0) Cr. 3. F. * Prereq: Com S 309, 319. * The requirements engineering process, including identification of stakeholders, requirements elicitation techniques such as interviews and prototyping, analysis fundamentals, requirements specification, and validation. Use of Models: State-oriented, Function-oriented, and Object-oriented. Documentation for Software Requirements. Informal, semi-formal, and formal representations. Structural, informational, and behavioral requirements. Non-functional requirements. Use of requirements repositories to manage and track requirements through the life cycle. Case studies, software projects, written reports, and oral presentations will be required. Nonmajor graduate credit.

** Com S 412. Formal Aspects of Specification and Verification. ** (Cross-listed with Cpr E, S E). (3-0) Cr. 3. * Prereq: Com S 309, 319. * Introduction to prepositional/predicate/temporal logic, program verification using theorem proving, model-based verification using model checking, and tools for verification. Nonmajor graduate credit.

** Com S 417. Software Testing. ** (Cross-listed with S E). (3-0) Cr. 3. S. * Prereq: Com S 309, 319. * Comprehensive study of software testing, principles, methodologies, management strategies and techniques. Test models, test design techniques (black box and white-box testing techniques), integration, regression, system testing methods, and software testing tools. Nonmajor graduate credit.

** Com S 418. Introduction to Computational Geometry. ** (Dual-listed with 518). (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 311 or permission of instructor, Engl 250, Sp Cm 212. * Introduction to data structures, algorithms, and analysis techniques for computational problems that involve geometry. Line segment intersection, polygon triangulation, 2D linear programming, range queries, point location, arrangements and duality, Voronoi diagrams and Delaunay triangulation, convex hulls, robot motion planning, visibility graphs. Other selected topics. Programming assignments. Nonmajor graduate credit.

** Com S 421. Logic for Mathematics and Computer Science. ** (Cross-listed with MAth). (3-0) Cr. 3. S. * Prereq: Math 301 or 307 or 317 or Com S 330. * Propositional and predicate logic. Topics selected from Horn logic, equational logic, resolution and unification, foundations of logic programming, reasoning about programs, program specification and verification, model checking and binary decision diagrams. Nonmajor graduate credit.

** Com S 425. High Performance Computing for Scientific and Engineering Applications. ** (Cross-listed with Cpr E). (3-1) Cr. 3. S. * Prereq: 311, 330, Engl 250, Sp Cm 212. * Introduction to high performance computing platforms including parallel computers and workstation clusters. Discussion of parallel architectures, performance, programming models, and software development issues. Sample applications from science and engineering. Practical issues in high performance computing will be emphasized via a number of programming projects using a variety of programming models and case studies. Oral and written reports. Nonmajor graduate credit.

** Com S 426. Introduction to Parallel Algorithms and Programming. ** (Dual-listed with 526). (Cross-listed with Cpr E). (3-2) Cr. 4. F. * Prereq: Cpr E 308 or Com S 321, Com S 311. * Models of parallel computation, performance measures, basic parallel constructs and communication primitives, parallel programming using MPI, parallel algorithms for selected problems including sorting, matrix, tree and graph problems, fast Fourier transforms. Nonmajor graduate credit.

** Com S 430. Advanced Programming Tools. ** (3-1) Cr. 3. F. * Prereq: 311, 362 or 363, Engl 250, Sp Cm 212. * Topics in advanced programming techniques and tools widely used by industry (e.g., event-driven programming and graphical user interfaces, standard libraries, client/server architectures and techniques for distributed applications). Emphasis on programming projects in a modern integrated development environment. Oral and written reports. Nonmajor graduate credit.

** Com S 440. Principles and Practice of Compiling. ** (Dual-listed with 540). (3-1) Cr. 3. Alt. S., offered 2009. * Prereq: 331, 342, Engl 250, Sp Cm 212. * Theory of compiling and implementation issues of programming languages. Programming projects leading to the construction of a compiler. Projects with different difficulty levels will be given for 440 and 540. Topics: lexical, syntax and semantic analyses, syntax-directed translation, runtime environment and library support. Written reports. Nonmajor graduate credit.

** Com S 444. Introduction to Bioinformatics. ** (Dual-listed with 544). (Cross-listed with BCB, Biol, Cpr E, Gen). (4-0) Cr. 4. F. * Prereq: Math 165 or Stat 401 or equivalent. * Broad overview of bioinformatics with a significant problem-solving component, including hands-on practice using computational tools to solve a variety of biological problems. Topics include: database searching, sequence alignment, gene prediction, RNA and protein structure prediction, construction of phylogenetic trees, comparative and functional genomics. Nonmajor graduate credit.

** Com S 454. Distributed and Network Operating Systems. ** (Dual-listed with 554). (Cross-listed with Cpr E). (3-1) Cr. 3. Alt. S., offered 2009. * Prereq: 311, 352, Engl 250, Sp Cm 212. * Laboratory course dealing with practical issues of design and implementation of distributed and network operating systems and distributed computing environments (DCE). The client server paradigm, inter-process communications, layered communication protocols, synchronization and concurrency control, and distributed file systems. Graduate credit requires additional in-depth study of advanced operating systems. Written reports. Nonmajor graduate credit.

** Com S 455. Simulation: Algorithms and Implementation. ** (Dual-listed with 555). (3-0) Cr. 3. F. * Prereq: 311 and 330, Stat 330, Engl 150, Sp Cm 212. * Introduction to discrete-event simulation with a focus on computer science applications, including performance evaluation of networks and distributed systems. Overview of algorithms and data structures necessary to implement simulation software. Discrete and continuous stochastic models, random number generation, elementary statistics, simulation of queuing and inventory systems, Monte Carlo simulation, point and interval parameter estimation. Graduate credit requires additional in-depth study of concepts. Oral and written reports. Nonmajor graduate credit.

** Com S 461. Database Systems Concepts and Internals. ** (3-1) Cr. 3. F. * Prereq: 311, Engl 250, Sp Cm 212 and Com S 363. * Data models. Algebraic, first order, and user oriented query languages. Data storage, access methods, query execution, and transaction management. Parallel and distributed databases. Special purpose databases. Information integration using data warehouses, mediators, wrappers, and data mining. Oral and written reports. Nonmajor graduate credit.

** Com S 471. Computational Linear Algebra and Fixed Point Iteration. ** (Cross-listed with MAth). (3-0) Cr. 3. F.S. * Prereq: Math 265 and either Math 266, or 267; knowledge of a programming language. * Computational error, solutions of linear systems, least squares, similarity methods for eigenvalues, solution of nonlinear equations in one and several variables. Nonmajor graduate credit.

** Com S 472. Principles of Artificial Intelligence. ** (Dual-listed with 572). (3-1) Cr. 3. F. * Prereq: 311, 330 or Cpr E 310, Stat 330, Engl 250, Sp Cm 212, Com S 342 or comparable programming experience. * Specification, design, implementation, and selected applications of intelligent software agents and multi-agent systems. Computational models of intelligent behavior, including problem solving, knowledge representation, reasoning, planning, decision making, learning, perception, action, communication and interaction. Reactive, deliberative, rational, adaptive, learning and communicative agents and multiagent systems. Artificial intelligence programming. Graduate credit requires a research project and a written report. Oral and written reports. Nonmajor graduate credit.

** Com S 474. Elements of Neural Computation. ** (3-1) Cr. 3. Alt. F., offered 2008. S. * Prereq: 311, 330 or Cpr E 310, Stat 330, Math 165, Engl 250, Sp Cm 212, Com S 342 or comparable programming experience. * Introduction to theory and applications of neural computation and computational neuroscience. Computational models of neurons and networks of neurons. Neural architectures for associative memory, knowledge representation, inference, pattern classification, function approximation, stochastic search, decision making, and behavior. Neural architectures and algorithms for learning including perceptions, support vector machines, kernel methods, bayesian learning, instance based learning, reinforcement learning, unsupervised learning, and related techniques. Applications in Artificial Intelligence and cognitive and neural modeling. Hands-on experience is emphasized through the use of simulation tools and laboratory projects. Oral and written reports. Nonmajor graduate credit.

** Com S 477. Problem Solving Techniques for Applied Computer Science. ** (Dual-listed with 577). (3-0) Cr. 3. F. * Prereq: 228; 330 or Cpr E 310, Math 166, Math 307 or Math 317, or consent of the instructor. * Selected topics in applied mathematics and modern heuristics that have found applications in areas such as geometric modeling, graphics, robotics, vision, human machine interface, speech recognition, computer animation, etc. Polynomial interpolation, roots of polynomials, resultants, solution of linear and nonlinear equations, approximation, data fitting, fast Fourier transform, linear programming, nonlinear optimization, Lagrange multipliers, genetic algorithms, integration of ODEs, curves, curvature, Frenet Formulas, cubic splines, and Bezier curves. Programming components. Written report for graduate credit. Nonmajor graduate credit.

** Com S 481. Numerical Solution of Differential Equations and Interpolation. ** (Cross-listed with MAth). (3-0) Cr. 3. S.SS. * Prereq: Math 265 and either Math 266 or 267; knowledge of a programming language. * Polynomial and spline interpolation, orthogonal polynomials, least squares, numerical differentiation and integration, numerical solution of ordinary differential equations. Nonmajor graduate credit.

** Com S 486. Fundamental Concepts in Computer Networking. ** (3-0) Cr. 3. S. * Prereq: 352. * An introduction to fundamental concepts in the design and implementation of computer communication in both the wired and wireless networks, their protocols, and applications. Layered network architecture in the Internet, applications, transport, Socket APIs, network, and data link layers and their protocols, multimedia networking, and network security. Nonmajor graduate credit.

** Com S 490. Independent Study. ** Cr. arr. Repeatable. F.S. * Prereq: 6 credits in computer science, permission of instructor. * No more than 9 credits of Com S 490 may be counted toward graduation. Satisfactory-fail only.

H. Honors

**Courses primarily for graduate students, open to qualified undergraduate students**

** Com S 502. Complex Adaptive Systems Seminar. ** (Cross-listed with CAS). (1-0) Cr. 1. F.S. * Prereq: Admissions to CAS minor. * Understanding core techniques in artificial life are based on basic readings in complex adaptive systems. Understand techniques of complex system analysis methods including: Evolutionary computation, Neural nets, Agent based simulations (Agent based Computational Economics). Large-scale simulations are to be emphasized, e.g. power grids, whole ecosystems.

** Com S 503. Complex Adaptive Systems Concepts and Techniques. ** (Cross-listed with CAS). (3-0) Cr. 3. S. * Prereq: Admission to CAS minor or related field. * Survey of complex systems and their analysis. Examples are drawn from engineering, computer science, biology, economics and physics.

** Com S 509. Software Requirements Engineering. ** (Dual-listed with 409). (3-0) Cr. 3. F. * Prereq: 309. * The requirements engineering process including identification of stakeholders requirements elicitation techniques such as interviews and prototyping, analysis fundamentals, requirements specification, and validation. Use of Models: State-oriented, Function-oriented, and Object-oriented. Documentation for Software Requirements. Informal, semi-formal, and formal representations. Structural, informational, and behavioral requirements. Non-functional requirements. Use of requirements repositories to manage and track requirements through the life cycle. Case studies, software projects, written reports, and oral presentations will be required.

** Com S 511. Design and Analysis of Algorithms. ** (Cross-listed with Cpr E). (3-0) Cr. 3. F. * Prereq: Com S 311. * A study of basic algorithm design and analysis techniques. Advanced data structures, amortized analysis and randomized algorithms. Applications to sorting, graphs, and geometry. NP-completeness and approximation algorithms.

** Com S 512. Formal Methods in Software Engineering. ** (3-0) Cr. 3. S. * Prereq: 311, 330. * A study of formal techniques for specification and verification of software systems. Topics include temporal logic, propositional and predicate logic, model checking, process algebra, theorem proving. Tools providing automated support for these techniques will also be discussed.

** Com S 515. Software System Safety. ** (3-0) Cr. 3. F. * Prereq: 309 or 311, 342. * An introduction to the analysis, design, and testing of software for safety-critical and high-integrity systems. Analysis techniques, formal verification, fault identification and recovery, model checking, and certification issues. Emphasizes a case-based and systematic approach to software's role in safe systems.

** Com S 518. Introduction to Computational Geometry. ** (Dual-listed with 418). (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 311 or permission of instructor. * Introduction to data structures, algorithms, and analysis techniques for computational problems that involve geometry. Line segment intersection, polygon triangulation, 2D linear programming, range queries, point location, arrangements and duality, Voronoi diagrams and Delaunay triangulation, convex hulls, robot motion planning, visibility graphs. Other selected topics. Programming assignments. A scholarly report must be submitted for graduate credit.

** Com S 525. Numerical Analysis of High Performance Computing. ** (Cross-listed with Cpr E, MAth). (3-0) Cr. 3. S. * Prereq: Cpr E 308, or one of Math 471, 481; experience in scientific programming; knowledge of FORTRAN or C. * Development, analysis, and testing of efficient numerical methods for use on current state-of-the-art high performance computers. Applications of the methods to the students' areas of research.

** Com S 526. Introduction to Parallel Algorithms and Programming. ** (Dual-listed with 426). (Cross-listed with Cpr E). (3-2) Cr. 4. F. * Prereq: Cpr E 308 or Com S 321, Com S 311. * Models of parallel computation, performance measures, basic parallel constructs and communication primitives, parallel programming using MPI, parallel algorithms for selected problems including sorting, matrix, tree and graph problems, fast Fourier transforms.

** Com S 531. Theory of Computation. ** (3-0) Cr. 3. S. * Prereq: 331. * A systematic study of the fundamental models and analytical methods of theoretical computer science. Computability, the Church-Turing thesis, decidable and undecidable problems, and the elements of recursive function theory. Time complexity, logic, Boolean circuits, and NP-completeness. Role of randomness in computation.

** Com S 540. Principles and Practice of Compiling. ** (Dual-listed with 440). (3-1) Cr. 3. Alt. S., offered 2009. * Prereq: 331, 342, Engl 250, Sp Cm 212. * Theory of compiling and implementation issues of programming languages. Programming projects leading to the construction of a compiler. Projects with different difficulty levels will be given for 440 and 540. Topics: lexical, syntax and semantic analyses, syntax-directed translation, runtime environment and library support. Written reports.

** Com S 541. Programming Languages. ** (3-1) Cr. 3. F. * Prereq: 342 or 440. * Survey of the goals and problems of language design. Formal and informal studies of a wide variety of programming language features including type systems. Creative use of functional and declarative programming paradigms.

** Com S 544. Introduction to Bioinformatics. ** (Dual-listed with 444). (Cross-listed with BCB, Cpr E, GDCB). (4-0) Cr. 4. F. * Prereq: Math 165 or Stat 401 or equivalent. * Broad overview of bioinformatics with a significant problem-solving component, including hands-on practice using computational tools to solve a variety of biological problems. Topics include: database searching, sequence alignment, gene prediction, RNA and protein structure prediction, construction of phylogenetic trees, comparative and functional genomics.

** Com S 549. Advanced Algorithms in Computational Biology. ** (Cross-listed with BCB, Cpr E). (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 311 and either 228 or 208. * Design and analysis of algorithms for applications in computational biology, pairwise and multiple sequence alignments, approximation algorithms, string algorithms including in-depth coverage of suffix trees, semi-numerical string algorithms, algorithms for selected problems in fragment assembly, phylogenetic trees and protein folding. No background in biology is assumed. Also useful as an advanced algorithms course in string processing.

** Com S 550. Evolutionary Problems for Computational Biologists. ** (Cross-listed with BCB). (3-0) Cr. 3. F. * Prereq: Com S 311 and some knowledge of programming. * Discussion and analysis of basic evolutionary principles and the necessary knowledge in computational biology to solve real world problems. Topics include character and distance based methods, phylogenetic tree distances, and consensus methods, and approaches to extract the necessary information from sequence-databases to build phylogenetic trees.

** Com S 551. Computational Techniques for Genome Assembly and Analysis. ** (Cross-listed with BCB). (3-0) Cr. 3. F. * Prereq: 311 and some knowledge of programming. * Huang. Introduction to practical sequence assembly and comparison techniques. Topics include global alignment, local alignment, overlapping alignment, banded alignment, linear-space alignment, word hashing, DNA-protein alignment, DNA-cDNA alignment, comparison of two sets of sequences, construction of contigs, and generation of consensus sequences. Focus on development of sequence assembly and comparison programs.

** Com S 552. Principles of Operating Systems. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 352. * A comparative study of high-level language facilities for process synchronization and communication. Formal analysis of deadlock, concurrency control and recovery. Protection issues including capability-based systems, access and flow control, encryption, and authentication. Additional topics chosen from distributed operating systems, soft real-time operating systems, and advanced security issues.

** Com S 554. Distributed and Network Operating Systems. ** (Dual-listed with 454). (Cross-listed with Cpr E). (3-1) Cr. 3. Alt. S., offered 2009. * Prereq: 311, 352. * Laboratory course dealing with practical issues of design and implementation of distributed and network operating systems and distributed computing environments (DCE). The client server paradigm, inter-process communications, layered communication protocols, synchronization and concurrency control, and distributed file systems. Graduate credit requires additional in-depth study of advanced operating systems. Written reports.

** Com S 555. Simulation: Algorithms and Implementation. ** (Dual-listed with 455). (3-0) Cr. 3. F. * Prereq: Com S 311 and 330, Stat 330. * Introduction to discrete-event simulation with a focus on computer science applications, including performance evaluation of networks and distributed systems. Overview of algorithms and data structures necessary to implement simulation software. Discrete and continuous stochastic models, random number generation, elementary statistics, simulation of queuing and inventory systems, Monte Carlo simulation, point and interval parameter estimation. Graduate credit requires additional in-depth study of concepts. Oral and written reports.

** Com S 556. Analysis Algorithms for Stochastic Models. ** (3-0) Cr. 3. S. * Prereq: Com S 331, Math 307, and Stat 330. * Introduction to the use of stochastic models to study complex systems, including network communication and distributed systems. Data structures and algorithms for analyzing discrete-state models expressed in high-level formalisms. State space and reachability graph construction, model checking, Markov chain construction and numerical solution, computation of performance measures, product-form models, approximations, and advanced techniques.

** Com S 561. Principles of Database Systems. ** (3-0) Cr. 3. S. * Prereq: 311, 363. * Database models. Algebraic, first order, and user-oriented query languages. Database schema design. Physical storage, access methods, and query processing. Transaction management, concurrency control, and crash recovery. Database security. Parallel and distributed databases, and special purpose databases. Data warehousing and data mining.

** Com S 562. Implementation of Database Systems. ** (3-0) Cr. 3. F. * Prereq: 461 or 561. * Implementation topics and projects are chosen from the following: Storage architecture, buffer management and caching, access methods, design, parsing and compilation of query languages and update operations, application programming interfaces (APIs), user interfaces, query optimization and processing, and transaction management for relational, object-oriented, semistructured (XML), and special purpose database models; client-server architectures, metadata and middleware for database integration, web databases.

** Com S 567. Bioinformatics I (Fundamentals of Genome Informatics). ** (Cross-listed with BCB). (3-0) Cr. 3. F. * Prereq: Com S 208; Com S 330; Stat 341; credit or enrollment in Biol 315, Stat 401, and Stat 432. * Biology as an information science. Review of algorithms and information processing. Generative models for sequences. String algorithms. Pairwise sequence alignment. Multiple sequence alignment. Searching sequence databases. Genome sequence assembly.

** Com S 568. Bioinformatics II (Advanced Genome Informatics). ** (Cross-listed with BCB, GDCB, Stat). (3-0) Cr. 3. S. * Prereq: BCB 567, BBMB 301, Biol 315, Stat 401, Stat 432, credit or enrollment in Gen 411. * Advanced sequence models. Basic methods in molecular phylogeny. Hidden Markov models. Genome annotation. DNA and protein motifs. Introduction to gene expression analysis.

** Com S 569. Bioinformatics III (Structural Genome Informatics). ** (Cross-listed with BBMB, BCB, MAth, Cpr E). (3-0) Cr. 3. F. * Prereq: BCB 567, Gen 411, Stat 401, Stat 432. * Algorithmic and statistical approaches in structural genomics including protein, DNA and RNA structure. Structure determination, refinement, representation, comparison, visualization, and modeling. Analysis and prediction of protein secondary and tertiary structure, disorder, protein cores and surfaces, protein-protein and protein-nucleic acid interactions, protein localization and function.

** Com S 570. Bioinformatics IV (Computational Functional Genomics and Systems Biology). ** (Cross-listed with BCB, GDCB, Stat, Cpr E). (3-0) Cr. 3. S. * Prereq: BCB 567, Biol 315, Com S 363, Gen 411, Stat 401, Stat 432. * Algorithmic and statistical approaches in computational functional genomics and systems biology. Biological Information Integration - knowledge (ontology) driven and statistical approaches. Qualitative, probabilistic, and dynamic network models. Modeling, analysis, simulation and inference of transcriptional regulatory modules and networks, protein-protein interaction networks. Metabolic networks; cells and systems.

** Com S 572. Principles of Artificial Intelligence. ** (Dual-listed with 472). (3-1) Cr. 3. F. * Prereq: 311, 331, Stat 330, Com S 342 or comparable programming experience. * Specification, design, implementation, and selected applications of intelligent software agents and multi-agent systems. Computational models of intelligent behavior, including problem solving, knowledge representation, reasoning, planning, decision making, learning, perception, action, communication and interaction. Reactive, deliverative, rational, adaptive, learning and communicative agents. Artificial intelligence programming. Graduate credit requires a research project and a written report. Oral and written reports.

** Com S 573. Machine Learning. ** (3-1) Cr. 3. S. * Prereq: 311, 362, Stat 330. * Algorithmic models of learning. Design, analysis, implementation and applications of learning algorithms. Learning of concepts, classification rules, functions, relations, grammars, probability distributions, value functions, models, skills, behaviors and programs. Agents that learn from observation, examples, instruction, induction, deduction, reinforcement and interaction. Computational learning theory. Data mining and knowledge discovery using artificial neural networks, support vector machines, decision trees, Bayesian networks, association rules, dimensionality reduction, feature selection and visualization. Learning from heterogeneous, distributed, dynamic data and knowledge sources. Learning in multi-agent systems. Selected applications in automated knowledge acquisition, pattern recognition, program synthesis, bioinformatics and Internet-based information systems. Oral and written reports.

** Com S 574. Intelligent Multiagent Systems. ** (3-0) Cr. 3. S. * Prereq: Stat 330, Com S 331, Com S 572 or Com S 573 or Com S 472 or Com S 474. * Specification, design, implementation, and applications of multi-agent systems. Intelligent agent architectures; infrastructures, languages and tools for design and implementation of distributed multi-agent systems; Multi-agent organizations, communication, interaction, cooperation, team formation, negotiation, competition, and learning. Selected topics in decision theory, game theory, contract theory, bargaining theory, auction theory, and organizational theory. Selected topics in knowledge representation and ontologies. Agent-based systems and the Semantic Web. Applications in distributed intelligent information networks for information retrieval, information integration, inference, and discovery from heterogeneous, autonomous, distributed, dynamic information sources.

** Com S 577. Problem Solving Techniques for Applied Computer Science. ** (Dual-listed with 477). (3-0) Cr. 3. F. * Prereq: 228; 330 or Cpr E 310, Math 166, Math 307 or Math 317, or consent of the instructor. * Selected topics in applied mathematics and modern heuristics that have found applications in areas such as geometric modeling, graphics, robotics, vision, human machine interface, speech recognition, computer animation, etc. Homogeneous coordinates and transformations, perspective projection, quaternions and rotations, polynomial interpolation, roots of polynomials, resultants, solution of linear and nonlinear equations, approximation, data fitting, Fourier series and fast Fourier transform, linear programming, nonlinear optimization, Lagrange multipliers, parametric and algebraic curves, curvature, Frenet formulas, Bezier curves. Programming components. A scholarly report is required for graduate credit.

** Com S 581. Computer Systems Architecture. ** (Cross-listed with Cpr E). (3-0) Cr. 3. F. * Prereq: Cpr E 381. * Quantitative principles of computer architecture design, instruction set design, processor architecture: pipelining and superscalar design, instruction level parallelism, memory organization: cache and virtual memory systems, multiprocessor architecture, cache coherency, interconnection networks and message routing, I/O devices and peripherals.

** Com S 583. Reconfigurable Computing Systems. ** (Cross-listed with Cpr E). (3-0) Cr. 3. * Prereq: Background in computer architecture, design, and organization. * Introduction to reconfigurable computing, FPGA technology and architectures, spatial computing architectures such as systolic and bit serial adaptive network architectures, static and dynamic rearrangeable interconnection architectures, processor architectures incorporating reconfigurabiltiy.

** Com S 586. Computer Network Architectures. ** (3-0) Cr. 3. F. * Prereq: 511, 552 or Cpr E 489. * Design and implementation of computer communication networks: layered network architectures, local area networks, data link protocols, distributed routing, transport services, network programming interfaces, network applications, error control, flow/congestion control, interconnection of heterogeneous networks, TCP/IP, ATM networks, multimedia communications, IP and application multicast, overlay networks, network security and web computing.

** Com S 587. Principles of Distributed and Network Programming. ** (3-0) Cr. 3. F. * Prereq: 352 or Cpr E 489 or equivalent. * Programming paradigms for building modern distributed applications, including multithreaded client-server programming, distributed object frameworks and programming languages. Directory services. Web-based computing. Mobile computing. Peer-to-Peer computing. Network multimedia applications. Reliability and manageability of networked systems, including aspects of distributed system security, verification of concurrent systems, and network management.

** Com S 590. Special Topics. ** Cr. arr. Repeatable. * Prereq: Permission of instructor. * Satisfactory-fail only.

** Com S 596. Genomic Data Processing. ** (Cross-listed with BCB, GDCB). (3-0) Cr. 3. F. * Prereq: Some knowledge of programming. * Study the practical aspects of genomic data processing with an emphasis on hand-on projects. Students will carry out major data processing steps using bioinformatics tools. Topics include base-calling, raw sequence cleaning and contaminant removal; shotgun assembly procedures and EST clustering methods; genome closure strategies and practices; sequence homology search and function prediction; annotation and submission of GenBank reports; and data collection and dissipation through the Internet. Important post-genomic topics like microarray data analysis and pathway database will also be covered.

** Com S 599. Creative Component. ** Cr. 1-3. Creative component for nonthesis option of Master of Science degree. Satisfactory-fail only.

**Courses for graduate students**

** Com S 610. Seminar. ** Cr. arr. Satisfactory-fail only.

** Com S 611. Advanced Topics in Analysis of Algorithms. ** (3-0) Cr. 3. Repeatable. Alt. S., offered 2009. * Prereq: 511, 531. * Advanced algorithm analysis and design techniques. Topics include graph algorithms, algebraic algorithms, number-theoretic algorithms, randomized and parallel algorithms. Intractable problems and NP-completeness. Advanced data structures.

** Com S 612. Distributed Algorithms. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 511 or 531. * The theory of distributed computation. Algorithms, lower bounds and impossibility results. Leader Elections, mutual exlusion, consensus and clock synchronization algorithms. Synchronous, asynchronous and partially synchronous distributed systems models. Shared memory and message passing systems. Fault-tolerance and randomization. Broadcast and multicast. Wait-free object simulations. Distributed shared memory.

** Com S 625. Issues in Parallel Programming and Performance. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 511. * Parallel solutions of numerical and non-numerical problems, implementation of parallel programs on parallel machines, performance and other computational issues in parallel programming.

** Com S 626. Parallel Algorithms for Scientific Applications. ** (Cross-listed with Cpr E). (3-0) Cr. 3. * Prereq: 526. * Algorithm design for high-performance computing. Applications to finite-element and finite difference methods for numerical simulations, sparse matrix computation, multidimensional tree data structure and particle-based methods, random numbers and Monte Carlo applications, algorithms for computational biology.

** Com S 631. Advanced Topics in Computational Complexity. ** (3-0) Cr. 3. Repeatable. Alt. F., offered 2008. * Prereq: 531. * Advanced study in the quantitative theory of computation. Time and space complexity of algorithmic problems. The structure of P, NP, PH, PSPACE, and other complexity classes, especially with respect to resource-bounded reducibilities and complete problems. Complexity relative to auxiliary information, including oracle computation and relativized classes, randomized algorithms, advice machines, Boolean circuits. Kolmogorov complexity and randomness.

** Com S 633. Advanced Topics in Computational Randomness. ** (3-0) Cr. 3. Repeatable. F. * Prereq: 531. * Advanced study of the role of randomness in computation. Randomized algorithms, derandomization, and probabilistic complexity classes. Kolmogorov complexity, algorithmic information theory, and algorithmic randomness. Applications chosen from cryptography, interactive proof systems, computational learning, lower bound arguments, mathematical logic, and the organization of complex systems.

** Com S 634. Theory of Games, Knowledge and Uncertainty. ** (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 330. * Fundamentals of Game Theory: individual decision making, strategic and extensive games, mixed strategies, backward induction, Nash and other equilibrium concepts. Discussion of Auctions and Bargaining. Repeated, Bayesian and evolutionary games. Interactive Epistemology: reasoning about knowledge in multiagent environment, properties of knowledge, agreements, and common knowledge. Reasoning about and representing uncertainty, probabilities, and beliefs. Uncertainty in multiagent environments. Aspects and applications of game theory, knowledge, and uncertainty in other areas, especially Artificial Intelligence and Economics, will be discussed.

** Com S 641. Advanced Topics in Programming Language Semantics. ** (3-0) Cr. 3. Repeatable. Alt. S., offered 2008. * Prereq: 531, 541. * Operational and other mathematical models of programming language semantics. Type systems and their soundness. Applications of semantics on areas such as program correctness, language design or translation.

** Com S 652. Advanced Topics in Distributed Operating Systems. ** (3-0) Cr. 3. Repeatable. F. * Prereq: 552. * Concepts and techniques for network and distributed operating systems: Communications protocols, processes and threads, name and object management, synchronization, consistency and replications for consistent distributed data, fault tolerance, protection and security, distributed file systems, design of reliable software, performance analysis.

** Com S 657. Advanced Topics in Computer Graphics. ** (3-0) Cr. 3. Alt. F., offered 2008. * Prereq: 228, IE/ME/CPR E/COM S 557. * Modern lighting models: Rendering Equation, Spherical Harmonics, Lafortune, Cook-Torrance. Non-polygonal primitives: volumes, points, particles. Textures: filtering, reflections creation. Graphics hardware: pipeline, performance inssues, programmability in vertex and fragment path. Per-pixel lighting. Nonphotorealistic rendering. Radiosity; Ray tracing.

** Com S 661. Advanced Topics in Database Systems. ** (3-0) Cr. 3. Repeatable. Alt. F., offered 2008. * Prereq: 461 or 561. * Advanced topics chosen from the following: database design, data models, query systems, query optimization, incomplete information, logic and databases, multimedia databases; temporal, spatial and belief databases, semistructured data, concurrency control, parallel and distributed databases, information retrieval, data warehouses, wrappers, mediators, and data mining.

** Com S 672. Advanced Topics in Computational Models of Learning. ** (3-0) Cr. 3. Repeatable. Alt. S., offered 2008. * Prereq: Com S 572 or 573 or 472 or 474. * Selected topics in Computational Learning Theory (PAC learning, Sample complexity, VC Dimension, Occam Learning, Boosting, active learning, Kolomogorov Complexity, Learning under helpful distributions, Mistake Bound Analysis). Selected topics in Bayesian and Information Theoretic Models (ML, MAP, MDL, MML). Advanced statistical methods for machine learning. Selected topics in reinforcement learning.

** Com S 673. Advanced Topics in Computational Intelligence. ** (3-0) Cr. 3. Repeatable. Alt. S., offered 2009. * Prereq: Com S 572 or 573 or 472 or 474. * Advanced applications of artificial intelligence in bioinformatics, distributed intelligent information networks and the Semantic Web. Selected topics in distributed learning, incremental learning, multi-task learning, multi-strategy learning; Graphical models, multi-relational learning, and causal inference; statistical natural language processing; modeling the internet and the web; automated scientific discovery; neural and cognitive modeling.

** Com S 681. Advanced Topics in Computer Architecture. ** (Cross-listed with Cpr E). (3-0) Cr. 3. Alt. S., offered 2009. * Prereq: 581. * Current topics in computer architecture design and implementation. Advanced pipelining, cache and memory design techniques. Interaction of algorithms with architecture models and implementations. Tradeoffs in architecture models and implementations.

** Com S 686. Advanced Topics in High-Speed Networks. ** (3-0) Cr. 3. Alt. S., offered 2008. * Prereq: 586. * Advanced topics in IP networks and optical networks. QoS routing and scheduling, multicast, multiprotocol label switching (MPLS), traffic engineering. Optical network architectures, routing and wavelength assignment algorithms, optical multicast, traffic grooming, optical burst switching, lightpath protection/restoration schemes, and IP over WDM.

** Com S 699. Research. ** Cr. arr. Repeatable. * Prereq: Approval of instructor. * Satisfactory-fail only.