Computer Science and Engineering
Courses for CSCE (CSCE)
CSCE
340/840
Numerical Analysis I LINKCrosslisted as MATH 340/840| Credit Hours: | 3 |
| Max credits per degree: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Algorithm formulation for the practical solution of problems, interpolation, roots of equations, differentiation, and integration. Effects of finite precision.
CSCE
410/810
Information Retrieval Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Outline of the general information retrieval problem, functional overview of information retrieval. Deterministic models of information retrieval systems; conventional Boolean, fuzzy set theory, p-norm, and vector space models. Probabilistic models. Text analysis and automatic indexing. Automatic query formulation. System-user adaptation and learning mechanisms. Intelligent information retrieval. Retrieval evaluation. Review of new theories and future directions. Practical experience with a working experimental information retrieval system.
CSCE
413/813
Database Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Data and storage models for database systems; entity/relationship, relational, and constraint models; relational databases; relational algebra and calculus; structured query language; Logical database design: normalization; integrity; distributed data storage; concurrency; security issues. Spatial databases and geographic information systems.
CSCE
421/821
Foundations of Constraint Processing LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Constraint processing for articulating and solving industrial problems such as design, scheduling, and resource allocation. The foundations of constraint satisfaction, its basic mechanisms (e.g., search, backtracking, and consistency-checking algorithms), and constraint programming languages. New directions in the field, such as strategies for decomposition and for symmetry identification.
CSCE
423/823
Design and Analysis of Algorithms LINK| Credit Hours: | 3 |
| Course Format: | Lecture |
| Course Delivery: | Classroom |
Mathematical preliminaries. Strategies for algorithm design, including divide-and-conquer, greedy, dynamic programming and backtracking. Mathematical analysis of algorithms. Introduction to NP-Completeness theory, including the classes P and NP, polynomial transformations and NP-complete problems.
CSCE
424/824
Computational Complexity Theory LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Turing machine model of computation: deterministic, nondeterministic, alternating, probabilistic. Complexity classes: Time and space bounded, deterministic, nondeterministic, probabilistic. Reductions and completeness. Complexity of counting problems. Non-uniformity. Lower bounds. Interactive proofs.
CSCE
425/825
Compiler Construction LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Review of program language structures, translation, loading, execution, and storage allocation. Compilation of simple expressions and statements. Organization of a compiler including compile-time and run-time symbol tables, lexical scan, syntax scan, object code generation, error diagnostics, object code optimization techniques, and overall design.
CSCE
428/828
Automata, Computation, and Formal Languages LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to the classical theory of computer science. Finite state automata and regular languages, minimization of automata. Context free languages and pushdown automata, Turing machines and other models of computation, undecidable problems, introduction to computational complexity.
CSCE
430/830
Computer Architecture LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Architecture of single-processor (Von Neumann or SISD) computer systems. Evolution, design, implementation, and evaluation of state-of-the-art systems. Memory Systems, including interleaving, hierarchies, virtual memory and cache implementations; Communications and I/O, including bus architectures, arbitration, I/O processors and DMA channels; and Central Processor Architectures, including RISC and Stack machines, high-speed arithmetic, fetch/execute overlap, and parallelism in a single-processor system.
CSCE
432/832
High-Performance Processor Architectures LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
CSCE 432 assumes knowledge of computer architecture, pipelining, memory hierarchy, instruction level parallelism, and compiler principles.
High performance computing at the processor level. The underlying principles and micro-architectures of contemporary high-performance processors and systems. State-of the-art architectural approaches to exploiting instruction level parallelism for performance enhancements. Case studies of actual systems highlight real-world trade-offs and theories.
CSCE
434/834
VLSI Design LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Prereqs:
CSCE 335 or permission.
Introduction to VLSI design using metal-oxide semiconductor (MOS) devices primarily aimed at computer science majors with little or no background in the physics or circuitry of such devices. Includes design of nMOS and CMOS logic, data-path, control unit, and highly concurrent systems as well as topics in design automation.
CSCE
435/835
Cluster and Grid Computing LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Build and program clusters. Cluster construction, cluster administration, cluster programming, and grid computing.
CSCE
436/836
Advanced Embedded Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Embedded hardware design techniques; transceiver design and low-power communication techniques; sensors and distributed sampling techniques; embedded software design and embedded operating systems; driver development; embedded debugging techniques;hardware and software architectures of embedded systems; and design, development, and implementation of embedded applications.
CSCE
437/837
File and Storage Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
System-level and device-level topics in the design, implementation, and use of file and storage systems. Components and organization of storage systems, disk drive hardware and firmware, multi-disk systems, RAID's, local distributed and P2P file systems, and low-power design.
CSCE
438/838
Sensor Networks LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Prereqs:
CSCE230 and CSCE310 or equivalent; senior or graduate standing or instructor permission.
Basics of sensor networks; theoretical and practical insight into wireless sensor networks, including low-power hardware and wireless communication principles; networking in wireless sensor networks; and applications of sensor networks, such as multimedia, underwater, and underground. A group project that provides hands-on interaction with a wireless sensor network testbed.
CSCE
439/839
Robotics LINK| Credit Hours: | 3 |
| Course Format: | Lab 2, Lecture 2 |
| Course Delivery: | Classroom |
Prereqs:
Fundamental theory and algorithms for real world robot systems. Design and build a robot platform and implement algorithms in C++ or other high level languages. Topics include: open and closed loop control, reactive control, localization, navigation, path planning, obstacle avoidance, dynamics, kinematics, manipulation and grasping, sensing, robot vision processing, and data fusion.
CSCE
441/841
Approximation of Functions LINKCrosslisted as MATH 441/841| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Polynomial interpolation, uniform approximation, orthogonal polynomails, least-first-power approximation, polynomial and spline interpolation, approximation and interpolation by rational functions.
CSCE
447/847
Numerical Analysis II LINKCrosslisted as MATH 447/847| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Numberical matrix methods and numerical solutions of ordinary differntial equations.
CSCE
451/851
Operating Systems Principles LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Organization and structure of operating systems. Control, communication, and synchronization of concurrent processes. Processor and job scheduling. Memory organization and management including paging, segmentation, and virtual memory. Resource management. Deadlock avoidance, detection, recovery. File system concepts and structure. Protection and security. Substantial programming.
CSCE
455/855
Distributed Operating Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Organization and structure of distributed operating systems. Control, communication and synchronization of concurrent processes in the context of distributed systems. Processor allocation and scheduling. Deadlock avoidance, detection, recovery in distributed systems. Fault tolerance. Distributed file system concepts and structure.
CSCE
456/856
Parallel Programming LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to the fundamentals of parallel computation and applied algorithm design. Methods and models of modern parallel computation; general techniques for designing efficient parallel algorithms for distributed and shared memory multiprocessor machines; principles and practice in programming an existing parallel machine.
CSCE
457/857
Systems Administration LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to basic concepts of system administration. Operating systems and networking overview. User and resource management. Networking, systems and internet related security. System services and common applications, web services, database services, and mail servers. Basic scripting in shell, Perl, and Expect. Systems administration on UNIX® platform.
CSCE
462/862
Communication Networks LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to the architecture of communication networks and the rudiments of performance modeling. Circuit switching, packet switching, hybrid switching, protocols, local and metro area networks, wide area networks and the Internet, elements of performance modeling, and network programming. Network security, asynchronous transfer mode (ATM), optical, wireless, cellular, and satellite networks, and their performance studies.
CSCE
464/864
Internet Systems and Programming LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Paradigms, systems, and languages for Internet applications. Client-side amd server-side programming, object-based and event-based distributed programmming, and multi-tier applications. Coverage of specific technologies varies.
CSCE
467/867
Software Quality LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Initial and ongoing software analysis, including metrics, requirements, correctness, performance, testing and validation. Frameworks and methods for software quality. Benchmarks and testing, processes for quality assurance, performance and quality models, software quality tools, testable designs and automated testing.
CSCE
470/870
Computer Graphics LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Display and recording devices; incremental plotters; point, vector, and character generation; grey scale displays, digitizers and scanners, digital image storage; interactive and passive graphics; pattern recognition; data structures and graphics software; the mathematics of three dimensions; homogeneous coordinates; projections and the hidden-line problem.
CSCE
471/871
Introduction to Bioinformatics LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Fundamentals and trends in bioinformatics. Scoring matrices and pairwise sequence alignments via dynamic programming, BLAST, and other heuristics. Multiple sequence alignments. Applications of machine learning methods such as hidden Markov models and support vector machines to biological problems such as family modeling and phylogeny.
CSCE
472/872
Digital Image Processing LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Digital imaging systems, digital image processing, and low-level computer vision. Data structures, algorithms, and system analysis and modeling. Digital image formation and presentation, image statistics and descriptions, operations and transforms, and system simulation. Applications include system design, restoration and enhancement, reconstruction and geometric manipulation, compression, and low-level analysis for computer vision.
CSCE
473/873
Computer Vision LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
High-level processing for image understanding and high-level vision. Data structures, algorithms, and modeling. Low-level representation, basic pattern-recognition and image-analysis techniques, segmentation, color, texture and motion analysis, and representation of 2-D and 3-D shape. Applications for content-based image retrieval, digital libraries, and interpretation of satellite imagery.
CSCE
474/874
Introduction to Data Mining LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Data mining and knowledge discovery methods and their application to real-world problems. Algorithmic and systems issues. Statistical foundations, association discovery, classification, prediction, clustering, spatial data mining and advanced techniques.
CSCE
475/875
Multiagent Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Distributed problem solving and planning, search algorithms for agents, distributed rational decision making, learning multiagent systems, computational organization theory, formal methods in Distributed Artificial Intelligence, multiagent negotiations, emergent behaviors (such as ants and swarms), and Robocup technologies and real-time coalition formation.
CSCE
476/876
Introduction to Artificial Intelligence LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to basic principles, techniques, and tools now being used in the area of machine intelligence. Languages for AI programming introduced with emphasis on LISP. Lecture topics include problem solving, search, game playing, knowledge representation, expert systems, and applications.
CSCE
477/877
Cryptography and Computer Security LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introductory course on cryptography and computer security. Topics: classical cryptography (substitution, Vigenere, Hill and permutation ciphers, and the one-time pad); Block ciphers and stream ciphers; The Data Encryption Standard; Public-key cryptography, including RSA and El-Gamal systems; Signature schemes, including the Digital Signature Standard; Key exchange, key management and identification protocols.
CSCE
478/878
Introduction to Machine Learning LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to the fundamentals and current trends in machine learning. Possible applications for game playing, text categorization, speech recognition, automatic system control, date mining, computational biology, and robotics. Theoretical and empirical analyses of decision trees, artificial neural networks, Bayesian classifiers, genetic algorithms, instance-based classifiers and reinforcement learning.
CSCE
479/879
Introduction to Neural Networks LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Introduction to the concepts, design and application of connection-based computing begins by simulating neural networks, focusing on competing alternative network architectures, including sparse distributed memories, Hopfield networks, and the multilayered feed-forward systems. Construction and improvement of algorithms used for training of neural networks addressed to reduce training time and improve generalization. Algorithms for training and synthesizing effective networks implemented in high level language programs running on conventional computers. Emphasis on methods for synthesizing and simplifying network architectures for improved generalization. Application areas include: pattern recognition, computer vision, robotics medical diagnosis, weather and economic forecasting.
CSCE
490/890
Special Topics in Computer Science LINK| Credit Hours: | 1-3 |
| Max credits per degree: | 6 |
| Course Format: | Lecture |
| Course Delivery: | Classroom |
Prereqs:
Permission.
Aspects of computers and computing for non-Computer Science and Computer Engineering majors and/or minors. Topics vary.
CSCE
496/896
Special Topics in Computer Science LINK| Credit Hours: | 1-3 |
| Max credits per degree: | 6 |
| Course Format: | Lecture |
| Course Delivery: | Classroom |
Prereqs:
Senior or graduate standing.
Aspects of computers and computing not covered elsewhere in the curriculum presented as the need arises.
CSCE
498/898
Computer Problems LINK| Credit Hours: | 1-6 |
| Max credits per degree: | 6 |
| Course Format: | Independent Study |
| Course Delivery: | Classroom, Web |
Prereqs:
Senior or graduate standing.
Independent project executed under the guidance of a member of the faculty of the Department of Computer Science. Solution and documentation of a computer problem demanding a thorough knowledge of either the numerical or nonnumerical aspects of computer science.
CSCE
891
Internship in Computer Practice LINK| Credit Hours: | 1 |
| Course Format: | Field |
| Campus: | |
| Course Delivery: | Classroom |
A detailed project proposal must be prepared by the student and approved by the department prior to the start of the project. A final report must be submitted.
Experiential learning in conjunction with an approved industrial or governmental agency under the joint supervision of an outside sponsor and a faculty member.
CSCE
897
Masters Project LINK| Credit Hours: | 1-6 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission of adviser
Designed for students pursuing a non-thesis option (Option III) to work on a project under the supervision of a member of the computer science and engineering faculty.
CSCE
899
Masters Thesis LINK| Credit Hours: | 6-10 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Admission to masters degree program and permission of major adviser
CSCE
910
Information Organization and Retrieval LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Aspects of natural language processing on digital computers. Analysis of information content by statistical, syntactic, and logical methods. Search and matching techniques. Automatic retrieval systems, question-answering systems. Evaluation of retrieval effectiveness.
CSCE
913
Advanced Topics in Database Systems LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Database system topics, coverage varying from year to year. Examples: Normalization theory; statistical databases; distributed databases; failure recovery; implementation issues. Readings in the current literature.
CSCE
914
Constraint Database Systems LINK| Credit Hours: | 3 |
| Course Format: | Lecture |
| Campus: | |
| Course Delivery: | Classroom |
Introduction to constraint database systems. Constraint data model, constraint query languages, query optimization and evaluation, constraint data storage and applications. Assignments in both use and the implementation of systems.
CSCE
921
Advanced Constraint Processing LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Prereqs:
CSCE421/821 or instructor permission
A continuation of the course on Foundations of Constraint Processing (CSCE 421/821). Intended for students with some sophistication and considerable interest in exploring methods for designing and using algorithms useful for solving combinatorial problems. The goal of the course is to study, analyze and critique seminal and recent research papers. Projects are optional.
CSCE
923
Development and Analysis of Efficient Algorithms LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Analysis of performance of algorithms on random access machines and Turing machines, data structures for design of efficient algorithms, sorting algorithms, divide and conquer strategies, algorithms on graphs and their performance bounds, pattern matching algorithms, achievable lower bounds on complexity, NP complete problems.
CSCE
924
Graph Algorithms LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Review concepts related to analysis of algorithms and graph theory. Classical graph theoretic algorithms including Eulerian paths, Hamiltonian circuits, shortest paths, network flows and traveling salesman. Planar graph algorithms. Theory of alternating chains and algorithms for graph matching problems. Approximate and parallel algorithms. Applications of graph algorithms to engineering and physical sciences.
CSCE
925
Scheduling Theory LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
Scheduling theory with particular emphasis to its application in computer science. Polynomial-time algorithms, NP-hardness proofs and analysis of heuristics. Minimization of makespan and mean flow time. Real-Time scheduling.
CSCE
930
Advanced Computer Architecture LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Recent advances in computer architecture including the effects of VLSI and methods of improving performance. Parallelism, pipelining, vector and array processors, multiprocessors and distributed processors, and data-flow architectures.
CSCE
932
Fault-Tolerance: Testing and Testable Design LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
CSCE 834 or permission
Increasing density of microelectronic circuits makes them harder to test during production and field operation. Theory and techniques developed to solve this problem. Faults and fault modeling; algorithms for test generation and fault simulation; built-in-self-test methods and standards; design for testability; and self-checking circuits.
CSCE
933
Fault-Tolerance: System Design and Analysis LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
CSCE 830 or permission
Theory and practice of creating extremely dependable digital systems through online fault-tolerance. Emphasizes modular redundancy in hardware and software to permit detection, masking, and removal of faulty components. Case studies from aerospace, banking, and other disciplines. Fault classification, error detection and diagnosis, dependability metrics, Byzantine Agreement, design trade-offs, and system simulation and modeling (esp. Markov).
CSCE
942
Numerical Analysis III LINKCrosslisted as MATH 942| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Advanced topics in numerical analysis.
CSCE
952
Advanced Computer Networks LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Advanced-level course on the recent development in computer networks. Integrated Services Digital Networks (ISDN), Broadband-ISDN and Asynchronous Transfer Mode (ATM), Multimedia Source and Traffic Characteristics, Source Policing, Scheduling and Quality of Service, Wireless Communication, Tracking of Mobile Users, Performance Computer networks.
CSCE
953
Optical Communication Networks LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
State-of-the-art optical communication networks, encompassing traditional networks operating on optical fiber and next-generation networks such as wavelength division multiplexed (WDM) and optical time division multiplexed (OTDM) networks. Fundamentals of optical network design, control, and management. Optical network design and modeling, routing and wavelength assignment algorithms, optical network simulation tools and techniques.
CSCE
961
Coding Theory LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
MATH 817 desirable
Channels, introduction to information theory, Shannon’s fundamental theorem, Linear codes, Hamming codes, Reed-Muller codes, cyclic codes, idempotents, BCH codes, Reed-Solomon codes, Quadratic residue codes, perfect single-error correcting codes, Sphere packings, the Golay codes, Lloyds theorem, nonexistence theorems, weight enumerators, the MacWilliams equation, association schemes, quasi-symmetric designs, polarities of designs, extension of graphs, self-orthogonal codes and designs.
CSCE
962
Advanced Software Engineering LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
Recent advances in the field of software engineering. Software reuse, artificial intelligence approaches to software design, usability and requirements engineering, and design environments. Computer tools for the design of software products. Analysis of software artifacts. Coordination in distributed software development. Readings from current software engineering literature discussed and evaluated. Students will participate in a group project which investigates specific software engineering research topics.
CSCE
970
Pattern Recognition LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Introduction to statistical decision theory, adaptive classifiers, supervised and nonsupervised training. Pattern recognition systems: transducers, feature extractors, decision units. Applications to optical character recognition, speech processing, remote sensing.
CSCE
971
Advanced Bioinformatics LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Advanced algorithmic techniques for bioinformatics. Development and analysis of string matching, graph theoretic and dynamic programming techniques applied to systems and computational biology problems such s multiple sequence alignment, alignment of DNA and protein sequences, genome rearrangements, and phylogeny and haplotypes.
CSCE
973
Support Vector Machines LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Campus: | |
| Course Delivery: | Classroom |
Core theory of the machine learning technique called support vector machines. Margin, kernels, and the formulation of a machine learning problem as an optimization problem that can be solved optimally. Implementation issues, kernel design, the appropriateness of various kernels for different applications, and regularization.
CSCE
974
Genetic Algorithms LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
For students taking CSCE 974, no biological sciences background is needed. However, a knowledge of genetic principles may help student to improve current algorithms.
Introduction of the motivation and current implementations of advanced genetic algorithms. These algorithms are built on basic principles borrowed from biology. Illustrates how a novel, implicitly-parallel search is implemented to obtain solutions for combinatorically-difficult problems.
CSCE
976
Advanced Artificial Intelligence LINK| Credit Hours: | 3 |
| Course Format: | Lecture 3 |
| Course Delivery: | Classroom |
Prereqs:
For students with some sophistication and considerable interest in exploring methods of designing and using algorithms useful for finding adequate answers to combinatorically large problems that require largely symbolic rather than numeric computing.
Study, analyze and critique basic and current research papers and to engage in artificial intelligence projects and experiments either alone or in small groups. Artificial intelligence environments, tools and expert system building. Class participation will be encouraged for the review of the more recent AI literature.
CSCE
977
Data Encryption LINK| Credit Hours: | 3 |
| Campus: | |
| Course Delivery: | Classroom |
History of public cryptology; elements of statistics, combinatorics, number theory, group theory; symmetric and asymmetric cryptosystems, “trap door” functions; public key cryptosystems; RSA and knapsack; levels of cryptographic security; computational complexity of algorithms; National Bureau of Standards-DES ( Standard); block and stream cyphers; cypher key management; protection of proprietary software and data.
CSCE
979
Advances in Neural Networks and Genetic Algorithms LINK| Credit Hours: | 3 |
| Course Format: | Lecture |
| Campus: | |
| Course Delivery: | Classroom |
CSCE 979 requires reading, research, and programming selected to address the open problems of improving the speed and robustness of algorithms for learning in networks and other self-organizing systems.
The state-of-the-art methods for supervised training of neural networks followed by the implementation and application of genetic algorithms. Evolution and self-organization of complex, adaptive, nonlinear systems for solving problems of pattern recognition, cognition, and control. Obtaining insight into the internal workings of neural networks. Current theories and experimental testing used for analysis and testing of connections and thresholds of trained neural networks. Reference materials include research reports, papers, and books on the theory and design of neural network based processors and problem solving systems.
CSCE
990
Seminar LINK| Credit Hours: | 1-3 |
| Max credits per degree: | 24 |
| Course Format: | Lecture |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Permission
Frontiers of an area of computer science.
CSCE
996
Research Problems Other Than Thesis LINK| Credit Hours: | 1-6 |
| Campus: | |
| Course Delivery: | Classroom |
Investigation of minor research problems to introduce graduate students to the methods of research in computer science by assigning a problem which is of research interest but within the capacity of a graduate student to complete within a semester.
CSCE
999
Doctoral Dissertation LINK| Credit Hours: | 1-24 |
| Max credits per degree: | 55 |
| Campus: | |
| Course Delivery: | Classroom |
Prereqs:
Admission to doctoral degree program and permission of supervisory committee chair
Description
For a brief description of the program, application requirements and contact information, view the graduate program summary.
Department Chair: Steve Goddard, Ph.D.
Graduate Committee Chair: Byrav Ramamurthy, Ph.D.
The Computer Science and Engineering (CSE) Department hosts advanced research programs in the general areas of
- Computer Science
- Computer Engineering
- Bioinformatics
Graduate students participate in research projects funded by major funding agencies and commercial companies.
The following graduate degree programs are available:
- Master of Science in Computer Science
- Master of Science in Computer Science with a Computer Engineering Specialization
- Master of Computer Science with a Bioinformatics Specialization
- Doctor of Philosophy in Computer Science
- Doctor of Philosophy in Engineering with a Computer Engineering Specialization
- Doctor of Philosophy in Computer Science with a Bioinformatics Specialization
- Joint Doctor of Philosophy in Computer Science and Mathematics
Specific information about Computer Science and Engineering graduate degree programs is available online at www.cse.unl.edu.
The CSE Department offers teaching assistantships and research assistantships to highly qualified students.
Master of Science.
Applicants for admission to the master of science degree program are required to submit scores for the general Graduate Record Examination and satisfy the general admission requirements of the Graduate College. Admission to full graduate standing in the MS program requires the equivalent of the undergraduate major in computer science. A TOEFL score of at least 600 (paper-based) and 250 (computer-based) is required for students whose native language is not English and who have not earned a baccalaureate in the US. Recommendation for admission to provisional standing in the MS program may be made in exceptional cases by the Computer Science Graduate Committee. Provisional admissions are limited by available space.
The master of science program may be carried out under Option I or Option III and conforms to the general requirements of the Graduate College. Students interested in computer engineering can take the computer engineering specialization within the master of science program.
Doctor of Philosophy.
Students applying for admission to the doctor of philosophy program in computer science must satisfy the general requirements for full graduate standing in the MS program as stated above. Admission to full graduate standing in the PhD program requires the successful completion of a qualifying examination. Admission to Candidacy for the PhD degree requires: the successful completion of a written comprehensive examination and the submission of an acceptable written proposal for the dissertation research to the student’s PhD Supervisory Committee.
Cooperative doctor of philosophy programs are also offered in conjunction with the Department of Mathematics and the College of Engineering.