The undergraduate curriculum in Computer Science leading to the Bachelor of Science degree is accredited by the Computing Accreditation Commission of ABET, http://www.abet.org. This degree equips students with a sound knowledge of the foundations of Computer Science as well as problem solving and system design skills necessary to create robust, efficient, reliable, scalable, and flexible software systems. The B.S. degree in Computer Science prepares students for graduate study in Computer Science and for various business, industry, and government positions including computer scientists, information technologists, and software developers. The main educational objectives of the Computer Science program at Iowa State University are that its graduates demonstrate expertise, engagement, and learning within three to five years after graduation.
•Expertise: Graduated students should have the ability to establish peer-recognized expertise in the discipline. They should have the ability to articulate this expertise by formulating and solving problems of interest, by creating or deriving value through the application of technology, and by using mathematical foundations, algorithmic principles, and computer science theory in designing, implementing, and evaluating computer-based systems and processes which meet the desired needs of their employers.
•Engagement: Graduated students should have the ability to be engaged in the profession through the practice of computer science in industry, academia, or the public sector. They should demonstrate effective teaming and commitment to working with others by applying communications skills and professional knowledge.
•Learning: Graduated students should have the ability to engage in sustained learning through graduate work, professional improvement opportunities, and self study so that they can adapt to the role played by information processing in ever-changing areas of science, technology, and society.
Curriculum in Computer Science
A student seeking a B.S. degree in Computer Science must satisfy the requirements of the University and College of Liberal Arts and Sciences (see Liberal Arts and Sciences, Curriculum) and the departmental requirements.
The departmental requirements consist of a minimum of 46 credits in Computer Science and satisfaction of written and oral requirements. Students must earn at least a C- in Math 165, Math 166, and each Computer Science course taken to fulfill the Degree Program. The LAS College requires the major must contain at least 8 credits in courses taken at Iowa State University that are numbered 300 or above and in which the student’s grade is C or higher.
The following courses are required:
| COM S 101 | Orientation | R |
| COM S 203 | Careers in Computer Science | R |
| COM S 227 | Object-oriented Programming | 4 |
| COM S 228 | Introduction to Data Structures | 3 |
| COM S 309 | Software Development Practices | 3 |
| COM S 311 | Introduction to the Design and Analysis of Algorithms | 3 |
| COM S 321 | Introduction to Computer Architecture and Machine-Level Programming | 3 |
| COM S 327 | Advanced Programming Techniques | 3 |
| COM S 331 | Theory of Computing | 3 |
| COM S 342 | Principles of Programming Languages | 3 |
| COM S 352 | Introduction to Operating Systems | 3 |
| COM S 402 | Computer Science Senior Project | 3 |
| At least 15 credits, including at least 6 credits of 400-level courses, all with a grade of C- or better, from the following: | ||
| COM S 319 | Construction of User Interfaces | 3 |
| COM S 336 | Introduction to Computer Graphics | 3 |
| COM S 362 | Object-Oriented Analysis and Design | 3 |
| COM S 363 | Introduction to Database Management Systems | 3 |
| COM S 409 | Software Requirements Engineering | 3 |
| COM S 410 | Distributed Development of Software | 3 |
| COM S 412 | Formal Methods in Software Engineering | 3 |
| COM S 413 | Foundations and Applications of Program Analysis | 3 |
| COM S 415 | Software System Safety | 3 |
| COM S 417 | Software Testing | 3 |
| COM S 418 | Introduction to Computational Geometry | 3 |
| COM S 421 | Logic for Mathematics and Computer Science | 3 |
| COM S 424 | Introduction to High Performance Computing | 3 |
| COM S 425 | High Performance Computing for Scientific and Engineering Applications | 3 |
| COM S 426 | Introduction to Parallel Algorithms and Programming | 4 |
| COM S 430 | Concurrent Programming in Practice | 3 |
| COM S 433 | Molecular Programming of Nanoscale Devices and Processes | 3 |
| COM S 435 | Algorithms for Large Data Sets: Theory and Practice | 3 |
| COM S 437 | Computer Game and Media Programming | 3 |
| COM S 440 | Principles and Practice of Compiling | 3 |
| COM S 441 | Programming Languages | 3 |
| COM S 444 | Bioinformatic Analysis | 4 |
| COM S 453 | Privacy Preserving Algorithms and Data Security | 3 |
| COM S 454 | Distributed Systems | 3 |
| COM S 455 | Simulation: Algorithms and Implementation | 3 |
| COM S 461 | Principles and Internals of Database Systems | 3 |
| COM S 472 | Principles of Artificial Intelligence | 3 |
| COM S 474 | Introduction to Machine Learning | 3 |
| COM S 476 | Motion Strategy Algorithms and Applications | 3 |
| COM S 477 | Problem Solving Techniques for Applied Computer Science | 3 |
| COM S 481 | Numerical Methods for Differential Equations | 3 |
| COM S 486 | Fundamental Concepts in Computer Networking | 3 |
| COM S 487 | Network Programming, Applications, and Research Issues | 3 |
| CPR E 431 | Basics of Information System Security | 3 |
| CPR E 458 | Real Time Systems | 3 |
| CPR E 489 | Computer Networking and Data Communications | 4 |
Com S 414 may not be applied towards fulfilling the 400-level electives.
Toward satisfying requirements of the College of Liberal Arts and Sciences, the following courses should be included:
| PHIL 343 | Philosophy of Technology | 3 |
| SP CM 212 | Fundamentals of Public Speaking | 3 |
| At least 17 credits of Math and Statistics | 17 | |
| MATH 165 | Calculus I | 4 |
| MATH 166 | Calculus II | 4 |
| COM S 230 | Discrete Computational Structures | 3 |
| One Statistics course from: | ||
| STAT 305 | Engineering Statistics | 3 |
| STAT 330 | Probability and Statistics for Computer Science | 3 |
| STAT 341 | Introduction to the Theory of Probability and Statistics I | 4 |
| At least one Math course from: | ||
| MATH 207 | Matrices and Linear Algebra | 3 |
| MATH 265 | Calculus III | 4 |
| MATH 266 | Elementary Differential Equations | 3 |
| MATH 267 | Elementary Differential Equations and Laplace Transforms | 4 |
| MATH 304 | Combinatorics | 3 |
| MATH 314 | Graph Theory | 3 |
| MATH 317 | Theory of Linear Algebra | 4 |
| One of the following 2-course Natural Science sequences (with labs): | ||
| BIOL 211 & 211L | Principles of Biology I and Principles of Biology Laboratory I | 4 |
| BIOL 212 & 212L | Principles of Biology II and Principles of Biology Laboratory II | 4 |
| BIOL 255 & 255L | Fundamentals of Human Anatomy and Fundamentals of Human Anatomy Laboratory | 4 |
| BIOL 256 & 256L | Fundamentals of Human Physiology and Fundamentals of Human Physiology Laboratory | 4 |
| CHEM 177 & 177L | General Chemistry I and Laboratory in General Chemistry I | 5 |
| CHEM 178 & 178L | General Chemistry II and Laboratory in College Chemistry II | 4 |
| GEOL 100 & 100L | How the Earth Works and How the Earth Works: Laboratory | 4 |
| GEOL 102 & 102L | History of the Earth and History of the Earth: Laboratory | 4 |
| PHYS 221 | Introduction to Classical Physics I | 5 |
| or PHYS 241 | Principles and Symmetries in Classical Physics I | |
| PHYS 222 | Introduction to Classical Physics II | 5 |
| or PHYS 242 | Principles and Symmetries in Classical Physics II | |
The following courses meet the communication proficiency requirement:
| LIB 160 | Information Literacy | 1 |
| ENGL 150 | Critical Thinking and Communication | 3 |
| ENGL 250 | Written, Oral, Visual, and Electronic Composition | 3 |
| One of the following | ||
| ENGL 302 | Business Communication | 3 |
| ENGL 305 | Creative Writing: Nonfiction | 3 |
| ENGL 309 | Proposal and Report Writing | 3 |
| ENGL 314 | Technical Communication | 3 |
According to the university-wide Communication Proficiency Grade Requirement, students must demonstrate their communication proficiency by earning a grade of C or better in ENGL 250. The Department requires a C or higher in the upper-level ENGL course (302, 305, 309, 314).
To obtain a bachelor's degree from the College of Liberal Arts and Sciences, curriculum in liberal arts and sciences, a student must earn at least 45 credits at the 300 level or above taken at a four-year college. All such credits, including courses taken on a pass/not pass basis, may be used to meet this requirement.
Students must take at least 15 credits of Computer Science courses at the 300 level or higher at Iowa State University while resident here. Computer Science transfer courses need to be a minimum grade of C or higher to be considered for course substitution.
Students must earn a C- or better in each Computer Science course which is a prerequisite to a course listed in the student's degree program.
Undergraduate Minor in Computer Science
The Department of Computer Science offers an undergraduate minor in Computer Science. The minor requires at least 16 credits in computer science courses. Com S 414 cannot be used to fulfill minor requirements.
A minimum grade of C- is required in Com S 227 and Com S 228. A minimum grade of C is required in both Com S 311 and the three credits of 300-level Computer Science courses and above. At least 6 credits of the minor must be in courses numbered 300 and above and taken at ISU with a grade of C or higher. The minor must include at least 9 credits that are not used to meet any other department, college, or university requirement.
| COM S 227 | Object-oriented Programming | 4 |
| COM S 228 | Introduction to Data Structures | 3 |
| COM S 230 | Discrete Computational Structures | 3 |
| COM S 311 | Introduction to the Design and Analysis of Algorithms | 3 |
| 3 credits in ComS courses at the 300 level or above | 3 | |
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.
Computer Science, B.S.
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| COM S 101 | 0 | COM S 228 | 3 |
| COM S 227 | 4 | COM S 230 | 3 |
| MATH 165 | 4 | MATH 166 | 4 |
| ENGL 150 | 3 | Social Science | 3 |
| LIB 160 | 1 | Foreign Language 102/Elective | 3-4 |
| Foreign Language 101/Elective | 3-4 | ||
| 15-16 | 16-17 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| COM S 203 | 0 | COM S 321 | 3 |
| COM S 311 | 3 | COM S 331 | 3 |
| COM S 327 | 3 | STAT 300-level elective | 3 |
| ENGL 250 | 3 | Social science | 3 |
| Natural Science 1 | 5 | Natural Science 2 | 5 |
| 14 | 17 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| COM S 309 | 3 | COM S 352 | 3 |
| COM S 342 | 3 | COM S 300-level elective | 3 |
| COM S 300-level elective | 3 | ENGL 300-level Elective | 3 |
| MATH elective | 3-4 | Arts and Humanities | 3 |
| SP CM 212 | 3 | Elective | 3 |
| 15-16 | 15 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| COM S 300-level Elective | 3 | COM S 402 | 3 |
| COM S 400-level Elective | 3 | COM S 400-level Elective | 3 |
| PHIL 343 | 3 | Arts and Humanities | 3 |
| Arts and Humanities | 3 | Social Science | 3 |
| Elective | 3 | Elective | 3 |
| 15 | 15 | ||
Graduate Study
The department offers graduate programs leading to degrees of Master of Science (MS) and Doctor of Philosophy (PhD) 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 for the students majoring in other disciplines.
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 31 semester credits is required. A thesis or a creative component 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 PhD requirements are governed by the student’s program of study committee within established guidelines of the department and the graduate college. They include coursework (demonstrating breadth and depth of knowledge), 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
Courses primarily for undergraduates:
Cr. R. F.S.
Introduction to the computer science discipline and code of ethics, Com S courses, research and networking opportunities, procedures, policies, help and computing resources, extra-curricular activities offered by the Department of Computer Science and Iowa State University. Discussion of issues relevant to student adjustment to college life. Offered on a satisfactory-fail basis only.
Offered on a satisfactory-fail basis only.
Cr. 4. F.S.SS.
Introduction to computer literacy and applications. Literacy: Impact of computer technology in today’s societies, hardware, software, software programming, database and information systems, communication and networks, digital media technology, computer security and safety, ethics and privacy. Applications: In-depth hands-on experience with the operating systems, Microsoft word processing, spreadsheets, database management and presentation software. No prior computer experience necessary. Offered online only. Attendance required at an orientation session the first week of class.
(1.5-1) Cr. 2. F.S.
Offered first 8 weeks and last 8 weeks. Use of personal computer and workstation operating systems and beginning programming. Project-oriented approach to computer operation and programming, including use of tools to aid in programming. Topics from computer history, using basic Windows and Unix tools, program structure, expression, variables, decision and logic, and iteration. No prior computer experience necessary.
Cr. 2.
Prereq: COM S 104
8-week course in programming, including instruction in syntax and semantics, of the following current programming languages.
(1-2) Cr. 2.
Prereq: COM S 104
8-week course in programming using Perl.
(2-0) Cr. 2.
Prereq: COM S 104
8-week course in programming using MATLAB.
(3-0) Cr. 3. F.S.
Introduction to web programming basics. Fundamentals of developing web pages using a comprehensive web development life cycle. Learn to code programs and earn in-depth experience with current web design techniques such as HTML5 and cascading style sheets. Programming with JavaScript, jQuery, PHP, SQL, and MySQL. Strategies for accessibility, usability and search engine optimization. No prior computer programming experience necessary.
(3-0) Cr. 3. F.S.
Introduction to computer programming for non-majors using a language such as the Visual Basic language. Basics of good programming and algorithm development. Graphical user interfaces.
(3-0) Cr. 3.
Prereq: COM S 107 or equivalent
Advanced programming applications in Visual Basic for non-majors. Emphasis on programming projects including sorting, file processing, database processing, web programming, and graphics and animation. Students will learn problem solving techniques and advanced programming skills to build real-world applications.
(2-2) Cr. 3. F.S.SS.
Using Microsoft Excel spreadsheets and Microsoft Access databases to input, store, process, manipulate, query, and analyze data for business and industrial applications.
Credit in Com S 113 may not be applied toward graduation in the COM S, S E, and CPR E majors.
(3-2) Cr. 4. F.S.
Prereq: Credit or Enrollment in MATH 140 or higher
Introduction to computer programming with an emphasis on problem solving. Topics include: program structures, expressions, variables, decision and logic, iteration, collections, input and output. Program construction and testing. Programming assignments including games and applications. No prior programming experience necessary. This course is intended for Computer Science majors.
Cr. R. F.S.
Computer science as a profession. Introduction to career fields open to computer science majors. Relationship of coursework to careers. Presentations by computer science professionals.
Offered on a satisfactory-fail basis only.
(Cross-listed with MIS). (3-1) Cr. 3. F.S.SS.
Prereq: MATH 150 or placement into MATH 140 or higher
An introduction to computer programming using an object-oriented programming language. Emphasis on the basics of good programming techniques and style. Extensive practice in designing, implementing, and debugging small programs. Use of abstract data types. Interactive and file I/O. This course is not designed for computer science, software engineering, and computer engineering majors.
Credit may not be applied toward graduation for both Com S 207/MIS 207 and Com S 227.
(3-1) Cr. 3.
Prereq: MIS/COM S 207, credit or enrollment in MATH 151, MATH 160, or MATH 165
Intermediate-level programming techniques. Emphasis on designing, writing, testing, debugging, and documenting medium-sized programs. Data structures and their uses. Dynamic memory usage. Inheritance and polymorphism. Algorithm design and efficiency: recursion, searching, and sorting. Event-driven and GUI programming. The software development process. This course is not designed for computer science, software engineering and computer engineering majors.
Credit may not be applied toward the major in computer science, software engineering, or computer engineering.
(3-2) Cr. 4. F.S.SS.
Prereq: Credit or Enrollment in MATH 143 or higher; recommended: a previous high school or college course in programming or equivalent experience.
Computer programming using objects as the mechanism for modularity, abstraction, and code reuse. Instance variables, methods, and encapsulation. Review of control structures for conditionals and iteration. Developing algorithms on strings, arrays, and lists. Recursion, searching, and sorting. Text parsing and file I/O. Interfaces, inheritance, polymorphism, and abstract classes. Exception handling. Tools for unit testing and debugging. Emphasis on a disciplined approach to specification, code development, and testing. Course intended for Com S majors.
Credit may not be applied toward graduation for both Com S 207 and 227.
(3-1) Cr. 3. F.S.SS.
Prereq: Minimum of C- in COM S 227, credit or enrollment in MATH 165
An object-oriented approach to data structures and algorithms. Object-oriented analysis, design, and programming, with emphasis on data abstraction, inheritance and subtype polymorphism, and generics. Abstract data type specification and correctness. Collections including lists, stacks, queues, trees, heaps, maps, hash tables, and graphs. Big-O notation and algorithm analysis. Searching and sorting. Graph search and shortest path algorithms. Emphasis on object-oriented design, writing and documenting medium-sized programs. This course is designed for majors.
(Cross-listed with MATH). (3-1) Cr. 3. F.S.SS.
Prereq: Minimum of C- in COM S 227 and MATH 165; ENGL 150
Concepts in discrete mathematics as applied to computer science. Logic, set theory, functions, relations, combinatorics, discrete probability, graph theory and number theory. Proof techniques, induction and recursion.
(3-0) Cr. 3. F.
Prereq: CPR E 185 or S E 185 or COM S 127 or COM S 207 or COM S 227
Introduction to installation, utilization, and administration of Linux systems. Topics include open-source software, package installation and management, shell programming and command-line utilities, process and service management, account management, network configuration, file sharing, interoperation with other computers and operating systems, automation, and system security.
Cr. arr. Repeatable, maximum of 6 credits. F.S.SS.
Prereq: Permission of instructor
No more than 6 credits of Com S 290 or Com S 290H may be counted toward graduation.
Cr. arr. Repeatable, maximum of 6 credits. F.S.
Prereq: Permission of instructor
No more than 6 credits of Com S 290 or Com S 290H may be counted toward graduation.
Cr. 1. Repeatable, maximum of 3 times. F.S.
Prereq: COM S 207 or COM S 227
Basics of problem solving using programming techniques. Development and implementation of simple to advanced data structures and algorithms, evaluation of problem difficulty, design and implementation of solutions, debugging, and working under time pressure.
Offered on a satisfactory-fail basis only.
(Cross-listed with S E). (3-1) Cr. 3. F.S.
Prereq: Minimum of C- in COM S 228 and MATH 165
A practical introduction to methods for managing software development. Process models, requirements analysis, structured and object-oriented design, coding, testing, maintenance, cost and schedule estimation, metrics. Programming projects.
(3-1) Cr. 3. F.S.SS.
Prereq: Minimum of C- in COM S 228; MATH 166, ENGL 150; COM S 230 or CPR E 310
Basic techniques for design and analysis of algorithms. Sorting, searching, graph algorithms, string matching, and NP-completeness. Design techniques such as dynamic programming, divide and conquer, greedy method, and approximation. Asymptotic, worst-case, average-case and amortized analyses. Topics from advanced data structures such as balanced trees and hashing.
(Cross-listed with S E). (3-0) Cr. 3. F.S.
Prereq: COM S 228
Overview of user interface design. Evaluation and testing of user interfaces. Review of principles of object orientation, object oriented design and analysis using UML in the context of user interface design. Design of windows, menus and commands. Developing Web and Windows-based user-interfaces. Event-driven programming. Introduction to Frameworks and APIs for the construction of user interfaces.
(3-1) Cr. 3. F.S.
Prereq: Minimum of C- in COM S 228 and MATH 165; COM S 230 or CPR E 281; ENGL 250
Introduction to computer architecture and organization. Emphasis on evaluation of performance, instruction set architecture, datapath and control, memory-hierarchy design, and pipelining. Assembly language programming.
Cr. 1. F.S.
Prereq: Minimum of C- in COM S 228; COM S 230
Half-semester course. Design and implementation of libraries and applications in C, for students with prior programming background. Emphasis on differences between C and other languages, including file I/O, string processing, memory management, and buffer overruns. Using build systems, debuggers, and other development tools. Programming projects.
(3-0) Cr. 3. F.S.
Prereq: Minimum of C- in COM S 228 and MATH 165
Object-oriented programming experience using a language suitable for exploring advanced topics in programming. Topics include memory management, parameter passing, inheritance, compiling, debugging, and maintaining programs. Significant programming projects.
(Cross-listed with LING). (3-1) Cr. 3. F.S.
Prereq: Minimum of C- in COM S 228, MATH 166, and in COM S 230 or CPR E 310; ENGL 250
Models of computation: finite state automata, pushdown automata and Turing machines. Study of grammars and their relation to automata. Limits of digital computation, unsolvability and Church-Turing thesis. Relations between classes of languages.
(3-0) Cr. 3. F.
Prereq: COM S 327, CoReq MATH 207 or MATH 317
Programming interactive computer graphics systems using standard low-level libraries (such as OpenGL or DirectX) with an emphasis on 3D rendering. The graphics pipeline and programmable shaders. Coordinate systems and transformations in two and three dimensions. Homogeneous coordinates, viewing transformations and perspective. Euler angles and quaternions. Visible surface algorithms. Lighting models and shading. Texture mapping, bump mapping, reflection, elementary ray tracing. Offscreen buffers, render-to-texture and related techniques.
(Cross-listed with S E). (3-1) Cr. 3. F.S.
Prereq: Minimum of C- in COM S 228 and MATH 165; COM S 230 or CPR E 310
Study of concepts in programming languages, especially functional programming concepts. Overview of major programming paradigms, their relationship, and tradeoffs among paradigms enabling sound choices of programming language for application-specific development. Programming projects.
(3-1) Cr. 3. F.S.
Prereq: COM S 321 or CPR E 381 and COM S 327 or CPR E 288; ENGL 250
Survey of operating system, networking and parallel programming issues. Introduction of processes, threads, process synchronization, deadlocks, memory, file systems, networking, security threats and encryption. Programming projects.
(3-0) Cr. 3. F.S.
Prereq: Minimum of C- in COM S 228 and MATH 165; ENGL 250
Relational, object-oriented, semistructured and query languages. SQL, XML, and NO-SQL. Database design using entity-relationship model, data dependencies, and relational database design. Application development in SQL-like languages and general purpose host languages with application program interfaces and a commonly used Object Relational Mapping framework. Web application development. Programming Projects.
Cr. R. Repeatable. F.S.SS.
Prereq: Permission of department chair
Required of all cooperative education students. Students must register for this course prior to commencing each work period.
Cr. 2-3. Repeatable, maximum of 6 credits. F.S.
Prereq: COM S 309, COM S 311, COM S 321, COM S 331, ENGL 250, SP CM 212, and Senior Classification
Students work as individuals and teams to complete the planning, design, and implementation of a significant project in the topic area. Oral and written reports.
No more than 6 credits of 402A, 402B, and 402C may be used toward graduation.
Cr. 2-3. Repeatable, maximum of 6 credits. F.
Prereq: COM S 309, COM S 311, COM S 321, COM S 331, and COM S 437, Senior Classification
Students conceive, plan, architect and design a computer game. Student registered in this course will work with students in ARTIS 409. Oral and written reports.
No more than 6 credits of 402A, 402B, and 402C may be used toward graduation.
Cr. 2-3. Repeatable, maximum of 6 credits. S.
Prereq: COM S 402A, Senior Classification
Students implement, test, and present a completed production computer game. Students in this class will work with students in ARTIS 409. Oral and written reports.
No more than 6 credits of 402A, 402B, and 402C may be used toward graduation.
(0-6) Cr. 2-3. Repeatable, maximum of 6 credits. F.S.
Prereq: COM S 309, COM S 311, COM S 321, and COM S 331, Senior Classification
Students work as individuals and teams to complete the planning, design, and implementation of a significant project in the topic area. Oral and written reports.
No more than 6 credits of 402A, 402B, and 402C may be used toward graduation.
(Dual-listed with COM S 509). (3-0) Cr. 3.
Prereq: COM S 309; for graduate credit: graduate standing or permission of instructor
The requirements engineering process including elicitation, requirements analysis fundamentals, requirements specification and communication, and requirements evaluation. Modeling of functional and nonfunctional requirements, traceability, and requirements change management. Case studies and software projects.
(Dual-listed with COM S 510). (3-0) Cr. 3.
Prereq: COMS 228, COMS 309, COMS 327; for graduate credit: graduate standing or permission of instructor
Teams of students develop software applications in a modern software engineering environment. Importance, effective processes pertaining to team organization, management and communication, and cultural issues of distributed development. Graduate credit requires in-depth study of concepts and oral presentations.
(Dual-listed with COM S 512). (Cross-listed with CPR E, S E). (3-0) Cr. 3.
Prereq: COM S 311; STAT 305 or STAT 330 or STAT 341; for graduate credit: graduate standing or permission of instructor
A study of formal techniques for model-based specification and verification of software systems. Topics include logics, formalisms, graph theory, numerical computations, algorithms and tools for automatic analysis of systems. Graduate credit requires in-depth study of concepts.
(Dual-listed with COM S 513). Cr. 3.
Prereq: COM S 342
Algorithms and tools for automatically reasoning about code and program executions to predict software behavior. Theory and foundations related to control flow analysis, dataflow analysis, abstract interpretation and symbolic execution. Applications of program analysis to improve software security, performance and testing. Concepts, algorithms, tools, benchmarks, methodologies for solving problems using program analysis and for preparing research in program analysis.
(Dual-listed with COM S 514). (3-0) Cr. 3.
Prereq: COM S 227 or COM S 207 or GERON 377 or ARTGR 271 or equivalent; for graduate credit: graduate standing or permission of instructor
Interdisciplinary course designed for students interested in assistive technology, pervasive computing, mobile computing and principles of universal and inclusive design for end users, in particular, the elderly population. Students work in semester-long projects as interdisciplinary teams to apply knowledge obtained from lectures and mutual presentations. Research report and oral presentation required for graduate credit.
(Dual-listed with COM S 515). (3-0) Cr. 3.
Prereq: COM S 309 or COM S 311; for graduate credit: graduate standing or permission of instructor
An introduction to the hazard analysis, safety requirements, design, and testing of software for safety-critical and high-dependability systems. Safety analysis techniques, fault identification and recovery, and certification issues. Emphasizes a case-based and systematic approach to software's role in safe systems.
(Dual-listed with COM S 518). (3-0) Cr. 3.
Prereq: COM S 311; for graduate credit: graduate standing or permission of instructor
Introduction to data structures, algorithms, and analysis techniques for computational problems that involve geometry. Convex hulls, line segment intersection, polygon triangulation, 2D linear programming, range queries, point location, arrangements and duality, Voronoi diagrams, Delaunay triangulations, geometric data structures, robot motion planning, visibility graphs. Other selected topics. Programming assignments. Scholarly report required for graduate credit.
(Cross-listed with MATH). (3-0) Cr. 3.
Prereq: MATH 301 or MATH 207 or MATH 317 or COM S 230 or CPR E 310
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, temporal logic and modal logic.
(Cross-listed with CPR E). (2-2) Cr. 3.
Prereq: COM S 311, 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.
(Dual-listed with COM S 526). (Cross-listed with CPR E). (3-2) Cr. 4. F.
Prereq: CPR E 308 or COM S 321, CPR E 315 or 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.
(3-1) Cr. 3.
Prereq: COM S 311, COM S 362 or COM S 363, ENGL 250, SP CM 212
A practical course in concepts, techniques, languages, and frameworks for concurrent and asynchronous systems. Concurrency fundamentals: threads, synchronization locks, waiting and notification, memory visibility. Immutability and thread confinement. Concurrent data structures and utilities, thread pools. Asynchronous programming with callbacks, futures, and message passing. Issues of aliasing, ownership and borrowing. Transactional memory, immutable and versioned data structures. Alternatives to threads and locks: event-driven systems, the actor model, CSP, coroutines. Students will investigate several non-mainstream languages supporting different concurrency models. Oral and written reports.
(Dual-listed with COM S 533). (3-0) Cr. 3.
Prereq: Minimum of C- in COM S 331 or permission of instructor; for graduate credit: graduate standing or permission of instructor
Programming, modeling, and analysis of natural and engineered systems at the nanoscale. Topics include chemical reaction networks, strand displacement systems, models of self-assembly, biomolecular origami, and molecular robotics. Emphasis on mathematical methods of describing, simulating, programming, and assessing the computational power of such systems. Graduate credit requires a written or oral report on current research.
(Dual-listed with COM S 535). (3-0) Cr. 3.
Prereq: COM S 311 or equivalent; for graduate credit: graduate standing or permission of instructor
Algorithmic challenges involved in solving computational problems on massive data sets. Probabilistic data structures, Curse of Dimensionality and dimensionality reduction, locality sensitive hashing, similarity measures, matrix decompositions. Optimization problems in massive data analysis. Computational problems that arise in the context of web search, social network analysis, online advertising etc. Practical aspects include implementation and performance evaluation of the algorithms on real world data sets. Graduate credit requires a written report on current research.
(3-0) Cr. 3.
Prereq: COM S 336
Video game programming using current game engine interfaces with real hardware. Particular attention is paid to the development environment, tool chains, 2D graphics, 3D graphics, controllers, memory management, and audio systems.
(Dual-listed with COM S 540). (3-1) Cr. 3.
Prereq: COM S 331 or COM S 342; ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
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 include: lexical, syntactic and semantic analyses, syntax-directed translation, code generation, runtime environment and library support.
(Dual-listed with COM S 541). (3-1) Cr. 3.
Prereq: COM S 342 or COM S 440; for graduate credit: graduate standing or permission of instructor
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.
(Cross-listed with BCB, BCBIO, BIOL, CPR E, GEN). (4-0) Cr. 4. F.
Prereq: MATH 165 and Introductory Statistics (STAT 101, STAT 104, STAT 105, STAT 201, or STAT 330).
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: bioinformatic data processing, Python programming, genome assembly, database search, sequence alignment, gene prediction, next-generation sequencing, comparative and functional genomics, and systems biology.
Cr. 3.
Prereq: COM S 311
Fundamentals of privacy preserving algorithms, data security, anonymization, and techniques and mechanisms to minimize disclosure of sensitive information while maintaining availability. Theory and fundamentals underpinning measures to evaluate the privacy and availability of data; implementation and deployment of privacy-preserving data operations including pre- and post-randomization techniques, homomorphisms, and secure function evaluation protocols. Theory and practice of the algorithmic limits on data privacy, including the cost in terms of time and space complexity.
(Dual-listed with COM S 554). (Cross-listed with CPR E). (3-1) Cr. 3.
Prereq: COM S 311; COM S 352 or CPR E 308; for graduate credit: graduate standing or permission of instructor
Theoretical and practical issues of design and implementation of distributed systems. The client server paradigm, inter-process communications, synchronization and concurrency control, naming, consistency and replication, fault tolerance, and distributed file systems. Graduate credit requires additional in-depth study of concepts. Programming projects and written reports.
(Dual-listed with COM S 555). (3-0) Cr. 3.
Prereq: COM S 311; STAT 305 or 330 or 341; ENGL 150, SP CM 212; for graduate credit: graduate standing or permission of instructor
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.
(Dual-listed with COM S 561). (3-1) Cr. 3.
Prereq: COM S 311, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
Database design including entity-relationship model, relational data model, and non-relational data models, data dependency, and normalization. Database management including physical storage, access methods, query processing, and transaction management. Database systems of special purposes such as spatial databases, mobile object databases, and multimedia databases. Introduction to current database research such as cloud data management and Internet information retrieval.
(Dual-listed with COM S 572). (3-1) Cr. 3.
Prereq: COM S 311, STAT 330 or STAT 305, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
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. Research project and written report required for graduate credit.
(Dual-listed with COM S 574). (3-1) Cr. 3.
Prereq: COM S 311, STAT 330 or STAT 305, MATH 165, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
Introduction to tools and techniques of machine learning for applications. Selected machine learning techniques in practical data mining for classification, regression, and clustering, e.g., association rules, decision trees, linear models, Bayesian learning, support vector machines, artificial neural networks, instance-based learning, probabilistic graphical models, ensemble learning, and clustering algorithms. Selected applications in data mining and pattern recognition.
(Dual-listed with COM S 576). Cr. 3.
Prereq: ENGL 250, SP CM 212, COM S 311
Recent techniques for developing algorithms that automatically generate continuous motions while satisfying geometric constraints. Applications in areas such as robotics and graphical animation. Basic path planning. Kinematics, configuration space, and topological issues. Collision detection. Randomized planning. Nonholonomic systems. Optimal decisions and motion strategies. Coordination of Multiple Bodies. Representing and overcoming uncertainties. Visibility-based motion strategies. Implementation of software that computes motion strategies. Written reports.
(Dual-listed with COM S 577). (3-0) Cr. 3.
Prereq: COM S 228; COM S 230 or CPR E 310, MATH 166, MATH 207 or MATH 317, or consent of the instructor; for graduate credit: graduate standing or permission of instructor
Selected topics in applied mathematics, algorithms, and geometry 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, rotations in space, quaternions, polynomial interpolation, roots of polynomials and polynomial systems, solution of linear and nonlinear equations, parametric and algebraic curves, curvature, torsion, Frenet formulas, surfaces, principal curvatures, Gaussian and mean curvatures, geodesics, approximation, Fourier series and fast Fourier transform, linear programming, data fitting, least squares, simplex method, nonlinear optimization, Lagrange multipliers, calculus of variations. Programming components. Scholarly report required for graduate credit.
(Cross-listed with MATH). (3-0) Cr. 3. S.
Prereq: MATH 265 and either MATH 266 or MATH 267
First order Euler method, high order Runge-Kutta methods, and multistep methods for solving ordinary differential equations. Finite difference and finite element methods for solving partial differential equations. Local truncation error, stability, and convergence for finite difference method. Numerical solution space, polynomial approximation, and error estimate for finite element method. Computer programming required.
(3-0) Cr. 3.
Prereq: COM S 352 or CPR E 308
An introduction to fundamental concepts in the design and implementation of computer communication in both wired and wireless networks, their protocols, and applications. Layered network architecture in the Internet, applications, transport, network, and data link layers and their protocols, Socket API, software-defined networking, and network security.
(Dual-listed with COM S 587). (3-0) Cr. 3.
Prereq: COM S 352 or CPR E 489 or equivalent; for graduate credit: graduate standing or permission of instructor
Programming paradigms for building distributed and networking applications, including multithreaded client-server programming, socket programming, distributed object frameworks and programming suites, and web computing and security. Introduction to some on-going research issues in distributed and networking applications, including peer-to-peer computing, multimedia communications, and mobile computing and networking. Written report and oral presentation required for graduate credit.
Cr. arr. Repeatable, maximum of 9 credits. F.S.SS.
Prereq: 6 credits in computer science, permission of instructor
No more than 9 credits of Com S 490 or Com S 490H may be counted toward graduation.
Cr. arr. Repeatable, maximum of 9 credits. F.S.
Prereq: 6 credits in computer science, permission of instructor
No more than 9 credits of Com S 490 or Com S 490H may be counted toward graduation.
Courses primarily for graduate students, open to qualified undergraduates:
(Dual-listed with COM S 409). (3-0) Cr. 3.
Prereq: COM S 309; for graduate credit: graduate standing or permission of instructor
The requirements engineering process including elicitation, requirements analysis fundamentals, requirements specification and communication, and requirements evaluation. Modeling of functional and nonfunctional requirements, traceability, and requirements change management. Case studies and software projects.
(Dual-listed with COM S 410). (3-0) Cr. 3.
Prereq: COMS 228, COMS 309, COMS 327; for graduate credit: graduate standing or permission of instructor
Teams of students develop software applications in a modern software engineering environment. Importance, effective processes pertaining to team organization, management and communication, and cultural issues of distributed development. Graduate credit requires in-depth study of concepts and oral presentations.
(Cross-listed with CPR E). (3-0) Cr. 3.
Prereq: COM S 311
A study of algorithm design and analysis techniques. Network flows and linear programming. Randomized algorithms. NP-completeness. Approximation algorithms. Fixed-parameter algorithms.
(Dual-listed with COM S 412). (3-0) Cr. 3.
Prereq: COM S 311; STAT 305 or STAT 330 or STAT 341; for graduate credit: graduate standing or permission of instructor
A study of formal techniques for model-based specification and verification of software systems. Topics include logics, formalisms, graph theory, numerical computations, algorithms and tools for automatic analysis of systems. Graduate credit requires in-depth study of concepts.
(Dual-listed with COM S 413). (Cross-listed with CPR E). Cr. 3.
Prereq: COM S 342
Algorithms and tools for automatically reasoning about code and program executions to predict software behavior. Theory and foundations related to control flow analysis, dataflow analysis, abstract interpretation and symbolic execution. Applications of program analysis to improve software security, performance and testing. Concepts, algorithms, tools, benchmarks, methodologies for solving problems using program analysis and for preparing research in program analysis.
(Dual-listed with COM S 414). (3-0) Cr. 3.
Prereq: COM S 227 or COM S 207 or GERON 377 or ARTGR 271 or equivalent; for graduate credit: graduate standing or permission of instructor
Interdisciplinary course designed for students interested in assistive technology, pervasive computing, mobile computing and principles of universal and inclusive design for end users, in particular, the elderly population. Students work in semester-long projects as interdisciplinary teams to apply knowledge obtained from lectures and mutual presentations. Research report and oral presentation required for graduate credit.
(Dual-listed with COM S 415). (3-0) Cr. 3.
Prereq: COM S 309 or COM S 311; for graduate credit: graduate standing or permission of instructor
An introduction to the hazard analysis, safety requirements, design, and testing of software for safety-critical and high-dependability systems. Safety analysis techniques, fault identification and recovery, and certification issues. Emphasizes a case-based and systematic approach to software's role in safe systems.
(Dual-listed with COM S 418). (3-0) Cr. 3.
Prereq: COM S 311; for graduate credit: graduate standing or permission of instructor
Introduction to data structures, algorithms, and analysis techniques for computational problems that involve geometry. Convex hulls, line segment intersection, polygon triangulation, 2D linear programming, range queries, point location, arrangements and duality, Voronoi diagrams, Delaunay triangulations, geometric data structures, robot motion planning, visibility graphs. Other selected topics. Programming assignments. Scholarly report required for graduate credit.
(Cross-listed with CPR E, MATH). (3-0) Cr. 3. S.
Prereq: CPR E 308 or MATH 481; experience in scientific programming; knowledge of FORTRAN or C
Introduction to parallelization techniques and numerical methods for distributed memory high performance computers. A semester project in an area related to each student’s research interests is required.
(Dual-listed with COM S 426). (Cross-listed with CPR E). (3-2) Cr. 4. F.
Prereq: CPR E 308 or COM S 321, CPR E 315 or 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.
(3-0) Cr. 3.
Prereq: COM S 331
A systematic study of the fundamental models and analytical methods of theoretical computer science. Computability, the Church-Turing thesis, decidable and undecidable problems. Computational resources such as time, space, and nonuniformity. Complexity classes, computational intractability and completeness. Selected topics from randomness, algorithmic information theory, and logic.
(Dual-listed with COM S 433). (3-0) Cr. 3.
Prereq: Minimum of C- in COM S 331 or permission of instructor; for graduate credit: graduate standing or permission of instructor
Programming, modeling, and analysis of natural and engineered systems at the nanoscale. Topics include chemical reaction networks, strand displacement systems, models of self-assembly, biomolecular origami, and molecular robotics. Emphasis on mathematical methods of describing, simulating, programming, and assessing the computational power of such systems. Graduate credit requires a written or oral report on current research.
(Dual-listed with COM S 435). (3-0) Cr. 3.
Prereq: COM S 311 or equivalent; for graduate credit: graduate standing or permission of instructor
Algorithmic challenges involved in solving computational problems on massive data sets. Probabilistic data structures, Curse of Dimensionality and dimensionality reduction, locality sensitive hashing, similarity measures, matrix decompositions. Optimization problems in massive data analysis. Computational problems that arise in the context of web search, social network analysis, online advertising etc. Practical aspects include implementation and performance evaluation of the algorithms on real world data sets. Graduate credit requires a written report on current research.
(Dual-listed with COM S 440). (3-1) Cr. 3.
Prereq: COM S 331 or COM S 342; ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
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 include: lexical, syntactic and semantic analyses, syntax-directed translation, code generation, runtime environment and library support.
(Dual-listed with COM S 441). (3-1) Cr. 3.
Prereq: COM S 342 or COM S 440; for graduate credit: graduate standing or permission of instructor
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.
(Cross-listed with BCB, CPR E, GDCB). (4-0) Cr. 4. F.
Prereq: MATH 165 or STAT 401 or equivalent
A practical, hands-on overview of how to apply bioinformatics to biological research. Recommended for biologists desiring to gain computational molecular biology skills. Topics include: sequence analysis, genomics, proteomics, phylogenetic analyses, ontology enrichment, systems biology, data visualization and emergent technologies.
(Cross-listed with CPR E). (3-0) Cr. 3.
Prereq: COM S 311 and either COM S 228 or COM S 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.
(3-0) Cr. 3.
Prereq: COM S 311 and some knowledge of programming
Introduction to a big data research area in bioinformatics. Focus on applying computational techniques to huge genomic sequence data. These techniques include finding optimal sequence alignments, generating genome assemblies, finding genes in genomic sequences, mapping short sequences onto a genome assembly, finding single-nucleotide and structural variations, building phylogenetic trees from genome sequences, and performing genome-wide association studies.
(3-0) Cr. 3.
Prereq: For graduate credit: graduate standing or permission of instructor
A comparative study of high-level language facilities for process synchronization and communication. 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. Programming and research projects.
(Dual-listed with COM S 454). (Cross-listed with CPR E). (3-1) Cr. 3.
Prereq: COM S 311; COM S 352 or CPR E 308; for graduate credit: graduate standing or permission of instructor
Theoretical and practical issues of design and implementation of distributed systems. The client server paradigm, inter-process communications, synchronization and concurrency control, naming, consistency and replication, fault tolerance, and distributed file systems. Graduate credit requires additional in-depth study of concepts. Programming projects and written reports.
(Dual-listed with COM S 455). (3-0) Cr. 3.
Prereq: COM S 311; STAT 305 or 330 or 341; ENGL 150, SP CM 212; for graduate credit: graduate standing or permission of instructor
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.
(3-0) Cr. 3.
Prereq: Graduate standing or permission of instructor
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.
(Cross-listed with CPR E, M E). (3-0) Cr. 3. Alt. S., offered odd-numbered years.
Prereq: M E 421, programming experience in C
Fundamentals of computer graphics technology. Data structures. Parametric curve and surface modeling. Solid model representations. Applications in engineering design, analysis, and manufacturing.
(Cross-listed with GEOL, HCI). (2-2) Cr. 3. Alt. F., offered even-numbered years.
Prereq: Graduate-student standing in the mathematical or natural sciences or engineering; basic programming knowledge
Introduction to visualizing scientific information with 3D computer graphics and their foundation in human perception. Overview of different visualization techniques and examples of 3D visualization projects from different disciplines (natural sciences, medicine, and engineering). Class project in interactive 3D visualization using the ParaView, Mayavi, TVTK, VTK or a similar system.
(Cross-listed with CPR E). Cr. 3.
Prereq: COM S 352 or CPR E 308, and COM S 486 or CPR E 489 or CPR E 530
Introduction to cloud computing concepts and systems. Security and privacy threats in cloud computing. Practical techniques for cloud computing security. Theoretical and practical solutions for secure outsourcing of data and computation. Oral presentations and research projects.
(Dual-listed with COM S 461). (3-1) Cr. 3.
Prereq: COM S 311, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
Database design including entity-relationship model, relational data model, and non-relational data models, data dependency, and normalization. Database management including physical storage, access methods, query processing, and transaction management. Database systems of special purposes such as spatial databases, mobile object databases, and multimedia databases. Introduction to current database research such as cloud data management and Internet information retrieval.
(Cross-listed with BCB, GDCB, STAT). (3-0) Cr. 3. S.
Prereq: BCB 567 or (BIOL 315 and one of STAT 430 or STAT 483 or STAT 583), credit or enrollment in GEN 409
Statistical models for sequence data, including applications in genome annotation, motif discovery, variant discovery, molecular phylogeny, gene expression analysis, and metagenomics. Statistical topics include model building, inference, hypothesis testing, and simple experimental design, including for big data/complex models.
(Cross-listed with BBMB, BCB, CPR E, GDCB). (3-0) Cr. 3. F.
Prereq: BCB 567, BBMB 316, GEN 409, STAT 430
Molecular structures including genes and gene products: protein, DNA and RNA structure. Structure determination methods, structural refinement, structure representation, comparison of structures, visualization, and modeling. Molecular and cellular structure from imaging. Analysis and prediction of protein secondary, tertiary, and higher order structure, disorder, protein-protein and protein-nucleic acid interactions, protein localization and function, bridging between molecular and cellular structures. Molecular evolution.
(Cross-listed with BCB, CPR E, GDCB, STAT). (3-0) Cr. 3. S.
Prereq: BCB 567 or COM S 311, COM S 228, GEN 409, STAT 430 or STAT 483 or STAT 583
Algorithmic and statistical approaches in computational functional genomics and systems biology. Analysis of high throughput biological data obtained using system-wide measurements. Topological analysis, module discovery, and comparative analysis of gene and protein networks. Modeling, analysis, and inference of transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks. Dynamic systems and whole-cell models. Ontology-driven, network based, and probabilistic approaches to information integration.
(Dual-listed with COM S 472). (3-1) Cr. 3.
Prereq: COM S 311, STAT 330 or STAT 305, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
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. Research project and written report required for graduate credit.
(3-1) Cr. 3.
Prereq: Graduate standing or permission of instructor
Basic principles, techniques, and applications of machine learning. Design, analysis, implementation, and applications of learning algorithms. Selected machine learning techniques in supervised learning, unsupervised learning, and reinforcement learning, including Bayesian decision theory, computational learning theory, decision trees, linear models, support vector machines, artificial neural networks, instance-based learning, probabilistic graphical models, ensemble learning, clustering algorithms, dimensionality reduction and feature selection. Selected applications in data mining and pattern recognition.
(Dual-listed with COM S 474). (3-1) Cr. 3.
Prereq: COM S 311, STAT 330 or STAT 305, MATH 165, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
Introduction to tools and techniques of machine learning for applications. Selected machine learning techniques in practical data mining for classification, regression, and clustering, e.g., association rules, decision trees, linear models, Bayesian learning, support vector machines, artificial neural networks, instance-based learning, probabilistic graphical models, ensemble learning, and clustering algorithms. Selected applications in data mining and pattern recognition.
(Cross-listed with CPR E, HCI). (3-0) Cr. 3. S.
Prereq: Graduate standing or permission of instructor
Statistical and algorithmic methods for sensing, recognizing, and interpreting the activities of people by a computer. Focuses on machine perception techniques that facilitate and augment human-computer interaction. Introduce computational perception on both theoretical and practical levels. Participation in small groups to design, implement, and evaluate a prototype of a human-computer interaction system that uses one or more of the techniques covered in the lectures.
(Dual-listed with COM S 476). Cr. 3.
Prereq: ENGL 250, SP CM 212, COM S 311
Recent techniques for developing algorithms that automatically generate continuous motions while satisfying geometric constraints. Applications in areas such as robotics and graphical animation. Basic path planning. Kinematics, configuration space, and topological issues. Collision detection. Randomized planning. Nonholonomic systems. Optimal decisions and motion strategies. Coordination of Multiple Bodies. Representing and overcoming uncertainties. Visibility-based motion strategies. Implementation of software that computes motion strategies. Written reports.
(Dual-listed with COM S 477). (3-0) Cr. 3.
Prereq: COM S 228; COM S 230 or CPR E 310, MATH 166, MATH 207 or MATH 317, or consent of the instructor; for graduate credit: graduate standing or permission of instructor
Selected topics in applied mathematics, algorithms, and geometry 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, rotations in space, quaternions, polynomial interpolation, roots of polynomials and polynomial systems, solution of linear and nonlinear equations, parametric and algebraic curves, curvature, torsion, Frenet formulas, surfaces, principal curvatures, Gaussian and mean curvatures, geodesics, approximation, Fourier series and fast Fourier transform, linear programming, data fitting, least squares, simplex method, nonlinear optimization, Lagrange multipliers, calculus of variations. Programming components. Scholarly report required for graduate credit.
Cr. 3.
Prereq: COM S 472, COM S 474, or instructor permission.
Advances in optimization theory and algorithms with evolving applications for machine learning. Theoretical foundations at the intersection of optimization and machine learning to conduct advanced research in machine learning and related fields. Emphasis on proof techniques for optimization algorithms in machine learning.
(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.
(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.
(3-0) Cr. 3.
Prereq: COM S 511, COM S 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, software-defined networking and network security.
(Dual-listed with COM S 487). (3-0) Cr. 3.
Prereq: COM S 352 or CPR E 489 or equivalent; for graduate credit: graduate standing or permission of instructor
Programming paradigms for building distributed and networking applications, including multithreaded client-server programming, socket programming, distributed object frameworks and programming suites, and web computing and security. Introduction to some on-going research issues in distributed and networking applications, including peer-to-peer computing, multimedia communications, and mobile computing and networking. Written report and oral presentation required for graduate credit.
Cr. arr. Repeatable.
Prereq: Permission of instructor
Special Topics in Computer Science.
Cr. 1.
Prereq: Graduate classification
Attend Computer Science Research Colloquia. Written summary is required.
Offered on a satisfactory-fail basis only.
Cr. R. Repeatable.
Prereq: Graduate Classification
Supervised internship working in professional settings appropriate to the student's degree program. Academic work under faculty supervision.
Cr. 1-3.
Creative component for nonthesis option of Master of Science degree.
Offered on a satisfactory-fail basis only.
Courses for graduate students:
Cr. arr.
Seminar in Computer Science.
Offered on a satisfactory-fail basis only.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 511, COM S 531
Advanced algorithm analysis and design techniques. Topics include, but are not limited to, graph algorithms, geometric algorithms, approximation algorithms, fixed-parameter algorithms, randomized algorithms and advanced data structures. Content varies by semester.
(3-0) Cr. 3.
Prereq: COM S 511 or COM S 531
The theory of distributed computation. Algorithms, lower bounds and impossibility results. Fundamental problems including consensus, leader election, mutual exclusion and clock synchronization. Synchronous, asynchronous and partially synchronous distributed systems models. Shared memory and message passing systems. Fault-tolerance and randomization. Wait-free object simulations. Distributed shared memory. Special topics vary from year to year.
(Cross-listed with CPR E). (3-0) Cr. 3.
Prereq: CPR E 526
Algorithm design for high-performance computing. Parallel algorithms for multidimensional tree data structures, space-filling curves, random number generation, graph partitioning and load balancing. Applications to grid and particle-based methods and computational biology.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 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. Novel models of computation emerging in a rapidly changing field.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 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. Novel models of computation emerging in a rapidly changing field.
(3-0) Cr. 3.
Prereq: COM S 230
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.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 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, and distributed file systems. Research project.
(3-0) Cr. 3. Repeatable, maximum of 2 times.
Prereq: COM S 228, I E 557/M E 557/CPR E 557/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 issues, programmability in vertex and fragment path. Per-pixel lighting. Nonphotorealistic rendering. Radiosity; Ray tracing.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 461 or COM S 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.
Cr. 3. Repeatable, maximum of 6 credits. Alt. F., offered irregularly.Alt. S., offered irregularly.
Prereq: COM S 511
Advanced topics on software repository analysis, data mining and software engineering, software engineering for context-aware and situation-aware computing, distributed development, product lines, safety, security, and reliability, and traceability. Content varies by semester.
Maximum 6 credits of COM S 665 may apply toward graduation.
Cr. 3. Repeatable, maximum of 6 credits. Alt. F., offered irregularly.Alt. S., offered irregularly.
Prereq: COM S 511
Advanced topics on theoretical and technical foundations in Software Engineering. Content varies by semester.
Maximum 6 credits of COM S 665 may apply toward graduation.
Cr. 3. Repeatable, maximum of 6 credits.
Prereq: COM S 511
Advanced topics on empirical studies on human factors and other software engineering topics. Content varies by semester.
Maximum 6 credits of COM S 665 may apply toward graduation.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 572 or COM S 573 or COM S 472 or COM S 474
Selected topics in probabilistic graphical models, causal inference, semantic web, information retrieval, natural language processing, knowledge representation and reasoning, deep learning, embedding, distributed learning, incremental learning, multi-task learning, multi-strategy learning, multi-relational learning, modeling the internet and the web, automated scientific discovery, neural and cognitive modeling. Advanced applications of artificial intelligence in bioinformatics, distributed systems, natural language, multimedia data, decision making, robotics, and more.
(3-0) Cr. 3. Repeatable.
Prereq: COM S 572 or COM S 573 or COM S 472 or COM S 474
Advanced topics in machine learning. Selected topics in computational learning theory, Bayesian and information theoretic models (ML, MAP, MDL, MML), probabilistic graphical models, statistical relational learning, reinforcement learning, and deep learning.
(Cross-listed with CPR E). (3-0) Cr. 3. Alt. S., offered odd-numbered years.
Prereq: CPR E 581. Repeatable with Instructor permission
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.
Cr. arr. Repeatable.
Prereq: Approval of instructor
Research.
Offered on a satisfactory-fail basis only.