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Courses

Computer and Information Sciences

5501. Programming and Data Structures   (3 s.h.)

Preparatory course for CIS graduate students who have an insufficient background in data structures and need a stronger foundation before taking the required core course CIS 8511: Programming Techniques. Note: Graduate credit will not apply for the CIS M.S./Ph.D. programs.

5502. System Software and Operating Systems   (3 s.h.)

Preparatory course for CIS graduate students who have an insufficient background in operating systems and need a stronger foundation before taking the required core course CIS 8512: Operating Systems. Note: Graduate credit will not apply for the CIS M.S./Ph.D. programs.

5503. Disc Structure of Computer Science   (3 s.h.)

Preparatory course for CIS graduate students who have an insufficient background in discrete structures and need a stronger foundation before taking the required core course CIS 8513: Automata and Formal Languages. Note: Graduate credit will not apply for the CIS M.S./Ph.D. programs.

8511. Programming Techniques   (3 s.h.)

Prerequisites: CIS 2168 and CIS 3223.

This course offers a more formalized view of data structures. Stacks, trees, tables, lists, multilinked structures, strings, and files are considered. These are viewed in terms of their general usefulness in the construction of algorithms and in their efficient implementation. Both theoretical results and programming techniques are stressed.

8512. Operating Systems   (3 s.h.)
Prerequisites: CIS 3207 and CIS 3223.

This course covers basic principles of operating systems, including control and coordination of tasks; deadlocks; design and implementation of operating systems in high-level languages; evaluation and prediction of performance; file systems; memory management, virtual memories, segmentation, and paging; multi-tasking systems; protection; resource management; and synchronization, mutual exclusion, and sharing.


8513. Automata and Formal Languages   (3 s.h.)
Prerequisites: CIS 2166 and CIS 3242.

This course covers closure properties and decidable problems of regular languages; context-sensitive languages and linear-bounded automata; derivation trees; finite automata and regular languages; Kleene's Theorem; methods of syntax analysis for context-free languages; normal forms of context-free grammar; self-embedding properties, closure properties, and decidable problems of context-free languages; Turing machines; and types of grammars.

8525. Neural Computation   (3 s.h.)
Prerequisites: STAT 8003 or STAT 8103 and undergraduate-level understanding of probability, statistics, and linear algebra.

Neural networks provide powerful techniques to model and control nonlinear and complex systems. The course is designed to provide an introduction to this interdisciplinary topic. The course is structured such that students from computer science, engineering, mathematics, physics, statistics, cognitive sciences, and other disciplines learn the main principles of this area as well as have an opportunity to explore promising research topics through hands-on experience with neural network simulators applied to classification and prediction problems ranging from biomedical sciences to finance and business.

8526. Machine Learning   (3 s.h.)
Prerequisites: STAT 8003 or STAT 8103 and undergraduate-level understanding of probability, statistics, and linear algebra.

The goal of the field of machine learning is to build computer systems that learn from experience and are able to adapt to their environments. This introductory machine learning course offers an overview of many techniques and algorithms in machine learning, beginning with topics such as simple concept learning and ending with more recent topics such as boosting, support vector machines, and reinforcement learning. The objective of the course is not only to present modern machine learning methods, but also to give the basic intuitions behind the methods as well as a more formal understanding of how and why they work.

8527. Data Warehousing, Filtering and Mining   (3 s.h.)
Prerequisites: CIS 8511 and an undergraduate course in databases.

The course is devoted to information system environments enabling efficient indexing and advanced analyses of current and historical data for strategic use in decision making. Data management is discussed in the content of data warehouses/data marts, Geographic Information Systems, Internet databases, mobile databases, and temporal and sequence databases. Constructs aimed at efficient online analytic processing (OLAP) and those developed for nontrivial exploratory analysis of current and historical data are discussed in detail. Theory is complemented by hands-on applied studies of problems in such fields as bioinformatics, e-commerce, financial engineering, and geosciences.

8535.  Probabilistic Graphical Models   (3 s.h.)

Prerequisites: CIS 2033 and CIS 3223. Basic familiarity with probability and statistics and programming capability using matlab are also required.

Probabilistic graphical models are important machine learning tools for knowledge representation and reasoning under uncertainty. They have been widely used in machine learning and related fields. This course provides an introduction to the theory, algorithms, and applications of probabilistic graphical models and covers the following topics: 1) Representation: joint distributions, random variables, and graphical model representations---directed and undirected graphical models; 2) Inference: basic inference algorithms; 3) Learning: parameter learning, structure learning, and learning with missing data; 4) Probabilistic Models: Bayesian networks, Markov networks, Gaussian networks, Hidden Markov models, and the like; and 5) Decision Making.

8536.  Ad Hoc Networks   (3 s.h.)

Prerequisites: CIS 3223 and either CIS 4319 or CIS 4329. Basic graph theory and fundamentals of computer networks are also required.

This course offers a comprehensive approach to fundamentals of ad hoc networks, including media access protocols, routing protocols, implementation, and communication performance.

8537.  Network and Information Security   (3 s.h.)

Prerequisite: CIS 4319 or CIS 4329 or an equivalent course in Computer Networks.

This course introduces basic knowledge of cryptography and its application to network and information security. With today’s increasing number of cyber attacks, cyber security becomes a critical research issue and practical concern. Network and information security constitute an important topic for computer and information science students.

8538.  Text Mining and Language Processing   (3 s.h.)

Prerequisites: CIS 3033 and undergraduate-level understanding of probability, statistics, and linear algebra.

This course offers a broad overview of problems and techniques in natural language processing and then covers the latest research in selected topics. The overview part of the course covers problems in: 1) Information Retrieval: building indexes, data compression, representation of queries and documents, and similarity functions; and 2) Information Extraction: building hierarchies of knowledge (ontologies), determining the meaning of words, and determining the relationships that exist between entities referred to in text. The in-depth part of the course focuses on the latest research in unsupervised information extraction. It covers such techniques as pointwise mutual information, pattern-matching, bootstrapping, Hidden Markov Models, Conditional Random Fields, statistical parsing, clustering, and language modeling, among others.

8539. Wireless Network and Communication   (3 s.h.)

Prerequisites: CIS 3207 and CIS 3223.

This course introduces the fundamental design and performance issues of wireless networks and communications. Topics covered include: 1) introduction; 2) Wireless System Design Fundamentals: channel assignment, handoffs, trunking efficiency, interference, frequency reuse, capacity planning, large-scale fading, and more; 3) path loss, small-scale fading, multipath, reflection, diffraction, scattering, shadowing, spatial-temporal channel modeling, and microcell/indoor propagation; 4) modulation, equalization, diversity, channel coding, and speech coding; 5) Bluetooth wearable computers, fixed wireless and Local Multipoint Distribution Service (LMDS), Wireless Local Area Networks, and other advanced technologies; 6) Wireless Cellular Networks: W-CDMA, cdma2000, GPRS, UMTS, and EDGE; 7) mobile ad hoc networks; 8) wireless sensor networks; and 9) WiMAX networks.

8542.   Computer Architecture   (3 s.h.)

Prerequisites: CIS 2168 and CIS 3207.

This course covers the recent developments in modern computer architectures and the emerging design methods for high-performance computing. Topics include: 1) computer abstraction and technology; 2) language of the computer; 3) algorithms for computers; 4) processors; 5) Large and Fast: Exploiting Memory Hierarchy; 6) storage and I/O topics; 7) multicores, multiprocessors, clusters and computing clouds; and 8) graphics and computing GPUs.

8543.  Computer Vision   (3 s.h.)

Prerequisites:  CIS 2033, CIS 3219, and CIS 3223.

The objective of the course is to introduce the theory and application of computer vision. The theoretic part introduces the analysis of visual patterns and the generative models behind them. The application

part uses real-world tasks to help students learn practical computer vision technologies. The course covers the following: image formation (camera model, color space, illumination model, etc.); low-level vision processing (edge detection, intensity-based segmentation, etc.); popular research tools in computer vision; visual matching and registration; visual recognition; image and category classification; scene understanding; object detection; visual tracking; activity and action analysis; and selected advanced topics. In addition to lectures, the course uses homework assignments, in-class discussions, and course projects.

8544. Distributed Systems   (3 s.h.)

Prerequisites: CIS 2166, CIS 2168, and CIS 8512.

This course considers a distributed computer system that consists of multiple autonomous processors that do not share primary memory but cooperate by sending messages over a communication network. Discussion of special problems is related to distributed control such as election and mutual exclusion, routing, data management, Byzantine agreement, and deadlock handling.

8590. Topics in Computer Science   (3 s.h.)
Prerequisite: Special authorization required.

Current topics and issues in Computer Sciences are covered.

8603.  Artificial Intelligence   (3 s.h.)

Prerequisites: CIS 2033, CIS 2166 or MATH 3098, and CIS 2168.

Since today's AI is mostly learning and inference, this course focuses on both subjects. It is designed as the first graduate course in learning and inference, introducing the basic concepts by focusing on their intuitive understanding and algorithmic perspective. The course is intended to prepare beginning graduate students for further graduate courses in machine learning, data mining, robotics, and computer vision. The course project focuses on programming and practical experiments with our high-end robot PekeeII from Wany Robotics.

9182. Independent Study   (1-6 s.h.)
Prerequisite: Permission of instructor.

Independent research is supervised by a Computer and Information Sciences faculty member.

9282. Independent Study   (1-6 s.h.)

Prerequisite: Permission of instructor.

Independent research is supervised by a Computer and Information Sciences faculty member.

9590. Seminar in Advanced Topics in Computer Science   (3 s.h.)
Prerequisite: Permission of instructor.

Topics are decided individually.

9601. Computer Graphics and Image Processing   (3 s.h.)
Prerequisite: CIS 8511.

This course provides analysis of techniques used in computer manipulation of two- and three-dimensional images. Although elements of computer graphics are covered (e.g., two- and three-dimensional transforms), the main focus is on image processing techniques. The student also gains insight into basic techniques in computer vision. Topics covered include image filters, image segmentation, similarity of images, object detection, object recognition, and shape representation and similarity. Today it is an easy task to transfer visual input of a camera to a computer's memory. However, image and video understanding belong still to unsolved problems of computer science. The main objective of the course is to convey basic intuitions behind the unsolved and solved problems and to introduce some of the techniques that provided solutions to some of the problems.

9602. User Interface Design and Systems Integration   (3 s.h.)
Prerequisite: CIS 8511.

This course focuses on the principles of usability engineering to design effective interfaces and as the basis for integrating existing systems to form new systems. The course builds on knowledge of networking, databases, and programming. The course outline is partly based on the recommendations of the ACM Special Interest Group on Computer-Human Interaction for an introductory graduate course on user interface design.

9615. Design and Analysis of Algorithms   (3 s.h.)
Prerequisites: CIS 8511 and CIS 8513.

This course deals with general techniques for algorithm development; divide and conquer Greedy dynamic programming; search and traverse; backtracking; and Branch and Bound. Some theoretical results are discussed, for example, those relating to NP - completeness.

9616. Principles of Data Management   (3 s.h.)
Prerequisites: CIS 8511 and CIS 8512.

This course covers fundamental and advanced topics in Database Management Systems, including: 1) Database System Architecture: ANSI/SPARC architecture; data abstraction; external, conceptual, and internal schemata; data independence; and data definition and data manipulation languages; 2) Data Models: entity-relationship and relational data model, data structures, integrity constraints, and operations for each data model; 3) Relational Query Languages: SQL, algebra, and calculus; 4) Theory of Database Design: functional dependencies, normal forms, dependency preservation, and information loss; 5) Query Optimization: equivalence of expressions, algebraic manipulation, and optimization of selections and joins; 6) Storage Strategies: indices, B-trees, and hashing; 7) Transaction Processing: recovery and concurrency control; 8) object-oriented and object-relational data model; 9) parallel and distributed databases; 10) multimedia databases and queries by content; and 11) data mining, data warehousing, mobile databases, and Web databases.

9617. Computer Networking and Communication   (3 s.h.)
Prerequisites: CIS 8511 and CIS 8512.

This course offers an introduction to the design and analysis of computer networks and communications systems, including the Physical, Data Link, Network, Transport, and Application layers. The Internet (TCP/IP) model is emphasized and compared and contrasted with other current technologies. Major themes include the distinction between service and protocol, performance metrics, analysis techniques, and fundamental performance tradeoffs.

9618. Web Applications Development   (3 s.h.)

Prerequisites: CIS 8511 and CIS 8512.

This course takes a technology-based approach to software engineering of networked application systems design and programming. It draws on new Microsoft .NET technology, together with former object-oriented design and programming, for its theoretical, architectural, and system design foundations. The course bridges software engineering principles in their most abstract and conceptual sense, with programming technique in its most concrete and pragmatic sense. It demonstrates how to optimize productivity of the software engineer, integrate the best that open technologies have to offer, and build large-scale systems that operate most efficiently on the internet. The course is divided into three parts: 1) theory that flows from .NET and object-oriented methodologies, including Application System Architectures, Design Methodologies, Quality Assurance, Scalability, and Security; 2) development of a working skill set in two .NET languages: ASP.NET and VB.NET and its major development tool, VS.NET; and 3) design and programming of a small but complete web-deployed application.

9651. Artificial Intelligence, Heuristic Models, and Education   (3 s.h.)
Prerequisite: CIS 9603.

This course offers an Introduction to how artificial intelligence and heuristic models are used to build better computer-based educational systems. Current as well as past models are explored (e.g., PROUST, GUIDON, SOAR, etc.). Key issues examined include student models, interfaces, pedagogical expertise, domain expertise, and collaborative learning systems.

9664. Knowledge Discovery and Data Mining   (3 s.h.)
Prerequisites: Basic knowledge in Data Base Systems (CIS 9616), programming skills in C or C++, and basic knowledge of statistics, graph theory, and linear algebra are required.

Basic concepts and techniques for the automated extraction of interesting patterns in large databases is discussed. Topics include association-rule mining, sequence mining, web and text mining, data warehousing, information filtering, classification and clustering analysis, Bayesian and neural networks, classification and regression trees, hypotheses evaluation, feature extraction, dimensionality reduction, singular value decomposition, data compression and reconstruction, visualization of large data sets, fractals in databases, and indexing methods that support efficient data mining and queries by content. Special emphasis is given in multimedia, business, scientific, and medical databases.

9665. Advanced Topics in Data Base Systems   (3 s.h.)
Prerequisite: CIS 9616.

This course surveys recent developments in database systems with an emphasis on object-oriented databases (OODB's). Prototype and operational OODB systems are analyzed. Applications of OODB's to computer-aided software engineering (CASE) environments, integrated application development environments, and geographical information systems are studied.

9666. Advanced Networks and Client-Server Computing   (3 s.h.)
Prerequisite: CIS 9617.

This course covers computer networks, network technology, protocols, routing algorithms, reliability, and design issues. Data transmission and transmission media, data communications fundamentals, transmission efficiency, wide-area networks, local area networks, wireless networks, TCP/IP and other protocol architectures, client/server computing, network management, and network security are studied.

9668.   Design and Development of E-Commerce Systems   (3 s.h.)

Prerequisite: CIS 9618.

The objective of this course is to teach the technical aspects of developing a commercial website, including the business-to-consumer and the business-to-business models. This process combines a number of integrated technologies: 1) Web page and style sheet design, 2) dynamic web pages that access data from relational and XML databases, 3) server-side transaction processing, 4) client/server and distributed processing, 5) principles of internet security, scalability, and database reliability, and 6) .NET programming. Students develop a site as a course project using two methodologies. One is custom coding using .NET; the other is to use Microsoft’s web site development system Commerce Server 2000. The first approach builds on the system started in the prerequisite course CIS 9618. The second approach uses a software tool that manages the entire process. Other tools to be used are Dreamweaver or FrontPage for web design, VS for .NET program development, and the Microsoft Enterprise Manager for the management of SQL Server databases. The course provides numerous online references to all of these languages and tools.

9669. Distributed and Parallel Computer Systems   (3 s.h.)
Prerequisite: CIS 9617.

This course is intended for students interested in the advances of scalable parallel computing systems. The main goal is to apply distributed and parallel computing theories to practical scalable parallel application development and new parallel programming tool construction.

9991. Project in Computer Science   (3 s.h.)
Prerequisites: Any four graduate-level courses and permission of the department.

The project is to be carried out under the direction of a Graduate Faculty member.

9994. Preliminary Exam Prep   (1-6 s.h.)

Prerequisites: Successful completion of the Qualifying Examination and permission of the  department.

9996. Thesis in Computer Science   (3-6 s.h.)
Prerequisites: Any four graduate-level courses and permission of the department.

The thesis is to be carried out under the direction of a Graduate Faculty member. Note: A total of 6 credits is required.

9998. Pre-Dissertation Research   (1-6 s.h.)
Prerequisites: Successful completion of the Preliminary I Examination and permission of the  department.

9999. Dissertation Research   (1-6 s.h.)
Prerequisites: Successful completion of the Preliminary II Examination and permission of the  department.

All Ph.D. candidates must enroll in this course for at least one to three credit hours each semester until the successful completion of the dissertation. May be repeated. Note: A minimum of 6 credits of CIS 9999 is required.

 

Updated 6.21.12