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.
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.
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.
8-week course in programming, including instruction in syntax and semantics, of the following current programming languages.
8-week course in programming using Perl.
8-week course in programming using MATLAB.
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.
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.
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.
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.
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.
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.
(Cross-listed with MATH). (3-1) Cr. 3. F.S.SS.
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.
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.
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.
(Cross-listed with S E). (3-1) Cr. 3. F.S.
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.
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.
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.
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.
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.
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.
Divisibility, integer representations, primes and divisors, linear diophantine equations, congruences, and multiplicative functions. Applications to cryptography. Additional topics, chosen at the discretion of the instructor.
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.
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. 2-3. Repeatable, maximum of 6 credits. F.S.
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.
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.
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.
(Dual-listed with COM S 509). (3-0) Cr. 3.
(Dual-listed with COM S 510). (3-0) Cr. 3.
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.
(Dual-listed with COM S 513). Cr. 3.
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.
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.
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.
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.
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, MATH). (2-2) Cr. 3. F.
Prereq: MATH 265; MATH 207 or MATH 317; or permission of instructor.
Unix, serial programming of scientific applications, OpenMP for shared-memory parallelization. No Unix, Fortran or C experience required.
(Cross-listed with CPR E). (2-2) Cr. 3.
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.
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.
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.
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.
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.
Students will learn 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.
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.
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.
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.
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.
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.
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.
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.
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 577). (3-0) Cr. 3.
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.
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.
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.
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.
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.
(Dual-listed with COM S 409). (3-0) Cr. 3.
(Dual-listed with COM S 410). (3-0) Cr. 3.
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.
(Dual-listed with COM S 413). (Cross-listed with CPR E). Cr. 3.
(Dual-listed with COM S 414). (3-0) Cr. 3.
(Dual-listed with COM S 415). (3-0) Cr. 3.
(Dual-listed with COM S 418). (3-0) Cr. 3.
(Cross-listed with CPR E, MATH). (3-0) Cr. 3. S.
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.
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.
(Dual-listed with COM S 435). (3-0) Cr. 3.
(Dual-listed with COM S 440). (3-1) Cr. 3.
(Dual-listed with COM S 441). (3-1) Cr. 3.
(Cross-listed with BCB, CPR E, GDCB). (4-0) Cr. 4. F.
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.
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.
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.
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.
(Dual-listed with COM S 455). (3-0) Cr. 3.
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. F.
(Cross-listed with GEOL, HCI). (2-2) Cr. 3. Alt. F., offered even-numbered years.
(Cross-listed with CPR E). Cr. 3.
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.
(Cross-listed with CPR E, INFAS). Cr. 3. Alt. S., offered irregularly.
Examination of applications of machine learning and big data techniques to various security and privacy problems, as well as secure and privacy-preserving machine learning algorithms.
(Dual-listed with COM S 461). (3-1) Cr. 3.
(Cross-listed with BCB, CPR E). (3-0) Cr. 3.
Biology as an information science. A review of the algorithmic principles that are driving the advances in bioinformatics and computational biology.
(Cross-listed with BCB, GDCB, STAT). (3-0) Cr. 3. S.
(Cross-listed with BBMB, BCB, CPR E, GDCB). (3-0) Cr. 3. F.
(Cross-listed with BCB, CPR E, GDCB, STAT). (3-0) Cr. 3. S.
(Dual-listed with COM S 472). (3-1) Cr. 3.
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.
(Cross-listed with CPR E, HCI). (3-0) Cr. 3. S.
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 477). (3-0) Cr. 3.
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.
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.
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.
Attend Computer Science Research Colloquia. Written summary is required. Offered on a satisfactory-fail basis only.
Supervised internship working in professional settings appropriate to the student's degree program. Academic work under faculty supervision.
Seminar in Computer Science. Offered on a satisfactory-fail basis only.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Research. Offered on a satisfactory-fail basis only.