Department of Electrical & Computer Engineering and Computer Science
College of Science, Engineering, and Technology

CSC 511 Object-Oriented Programming. (3 Hours): This course will cover the essentials for programming in an object-oriented language. Topics covered include: Classes, Interfaces, Inheritance and Polymorphism, Strings and Arrays, Library Packages, Input/Output, Exception Handling, Threads, Graphical User Interface and Event-driven programming.

CSC 512 Computer Architecture. (3 Hours) An advanced introduction to computer design and architecture. Topics include instruction set architecture, RISC computers, control unit design, pipelining, vector processing, memory system architecture, and classification of computers.

CSC 515 Data Structures and Algorithm Analysis. (3 Hours) Mathematical foundations for complexity theory, asymptotic notation, recurrence relations. Strategies for development of algorithms like divide and conquer, greedy, dynamic programming, backtracking. Exposure to some typical and important algorithms in computer science. Introduction to the theory of NP-completeness.

CSC 518 Operating Systems. (3 Hours) Emphasizes the concepts of process communication and synchronization, protection, performance measurement, and evaluation. Problems associated with mutual exclusion and synchronization, concurrent processes, information, process, device, and memory management are examined. Implementation of I/O and interrupt structure is also considered.

CSC 519 Principles of Programming Languages. (3 Hours) Important programming language concepts including, representation of data and sequence control, data abstraction and encapsulation; procedural and non-procedural paradigms: functional, logic, and object-oriented languages; distributed and parallel programming issues.

CSC 520 Database Management Systems. (3 Hours) Introduction to data base concepts including data independence; relations; logical and physical organizations; schema and subschema. Hierarchical, network, and relational models with description of logical and physical data structure representation of the database system. Normalization: first, second, and third normal forms of data relations. Relational algebra and relational calculus; data structures for establishing relations; query functions.

CSC 524 Computer Communication Networks and Distributed Processing. (3 Hours) Topologies, media selection, medium access control for local area networks (LANs) including high speed and bridged LANs; circuit switched, ISDN wide area networks (WANs) internetworking issues and standards, 150/051, TCP/IP protocols.

CSC 525 Wireless Ad hoc Networks. (3 Hours) This is a course on the fundamentals, design, architecture, protocols and applications of wireless ad hoc networks. The course will focus on the issues associated with the topology control, MAC layer, network layer, transport layer, security aspects, mobility models and energy consumption models of wireless ad hoc networks. The course will also look at the use of graph theory algorithms for simulating communication protocols in mobile ad hoc networks as well as data gathering protocols in wireless sensor networks.

CSC 526 Automata Theory. (3 Hours) Definition and representation of finite state automata and sequential machines. Equivalence of states and machines, congruence, reduced machines, and analysis and synthesis of machines. Decision problems of finite automata, partitions with the substitution property, generalized and complete machines, probabilistic automata, and other topics.

CSC 527 Real-Time Systems. (3 Hours) An introduction to the problems, concepts, and techniques involved in computer systems which must interface with external devices. These include process control systems, computer systems embedded within aircraft or automobiles, and graphics systems. The course concentrates on operating system software for these systems.

CSC 529 Compiler Construction. (3 Hours) An introduction to the major methods used in compiler implementation. The parsing methods of LL(k) and LR(k) are covered as well as finite state methods for lexical analysis, symbol table construction, internal forms for a program, run time storage management for block structured languages, and an introduction to code optimization.

CSC 530 Theory of Computation. (3 Hours) A survey of formal models for computation. Includes Turing Machines, partial recursive functions, recursive and recursively enumerable sets, abstract complexity theory, program schemes, and concrete complexity.

CSC 531 Computer Simulation Methods and Models. (3 Hours) A study and construction of discrete-system simulation models. Use of discrete-system simulation language (GPSS/H), advance programming techniques, random number generation, generation of various random variate, and statistical validation procedure.

CSC 533 Distributed Database System. (3 Hours) Prerequisites: CSC 520, 524. A consideration of the problems and opportunities inherent in distributed database on a networked computer system. Includes file allocation; directory systems; deadlock detection and prevention; synchronization; query optimization; and fault tolerance.

CSC 535 Information System Analysis and Design. (3 Hours) Prerequisite: 519. A practical guide to information systems programming and design. Theories relating to module design, coupling, and module strength are discussed. Techniques for reducing a system’s complexity are emphasized. The topics are oriented toward the experienced programmer or systems analyst.

CSC 537 Cloud Computing. (3 Hours) The course will present the state of the art in cloud computing technologies and applications as well as providing hands-on project opportunities and experiment with different technologies. Topics will include: telecommunications needs; architectural models for cloud computing; cloud computing platforms and services; security, privacy, and trust management; resource allocation and quality of service; cloud economics and business models; pricing and risk management; interoperability and internetworking; legal issues; and novel applications.

CSC 539 Special Topics in Computer Science. (Variable 1-9 Hours) Prerequisite: Consent of instructor. Topics and problems of information systems that are of practical importance and current interest. New developments in system concepts, techniques, and equipment.

CSC 540 Microcomputer Local Area Networks. (3 Hours) Prerequisites: 518. This course describes various criteria for selecting and implementing local area networks (LANs) consisting of microcomputers.

CSC 541 Cryptography and Network Security. (3 Hours) This course will focus on graduate-level topics in cryptography and network security, including: Symmetric Key and Public Key encryption algorithms, Digital Signatures, Certificates, Cryptanalysis, Key management and distribution, Classical network attacks and their solutions, User authentication protocols, Transport-level security, Wireless network security, E-mail security, Web security, IP security, Distributed system security, Firewalls and Intrusion detection systems.

CSC 545 Artificial Intelligence. (3 Hours) Efficient and intelligent search techniques. Knowledge representation e.g., logic, and semantic nets. Reasoning techniques including reasoning under uncertainty, e.g., fuzzy reasoning. Exposure to different artificial intelligence systems like planning and learning (including neural networks).

CSC 549 Applied Combinatorics and Graph Theory. (3 Hours) A study of combinatorial and graphical techniques for complexity analysis including generating functions, recurrence relations, Polyal’s theory of counting, planar directed graphs, and NP-complete problems. Applications of the techniques to the analysis of algorithms in graph theory, sorting, and searching.

CSC 551 Parallel and Distributed Computing. (3 Hours) Prerequisite: CSC 512 Computer Architecture or approval of Department. The course introduces the concepts and design of parallel and distributed computing systems. Topics covered include: Data versus control parallelism (SIMD/Vector, Pipelines, MIMD, Multi-core, GPU); Shared versus distributed memory (SMP and NUMA), Message passing Interface (MPI) and Topologies; Parallel and distributed algorithms: Paradigms, Models and Complexity, Scheduling, Synchronization, Deadlock detection, Fault tolerance and Load balancing.

CSC 552 Applied Programming. (3 Hours) Prerequisite: Department and advisor approval. This course focuses on the fundamentals of computing and is geared toward non-CS majors going into computational sciences. The course will cover key concepts of data structures, data manipulation, algorithms and efficiency, and how they apply to the various application domains specific to computational fields. The course will also provide an introduction to Python for computational sciences. Topics include: an introduction to computational complexity, data structures (arrays, lists, stacks, queues, trees, and graphs), elementary algorithms and their complexity.

CSC 555 Information Storage and Retrieval. (3 Hours) Advanced data structures, databases, and processing systems for access and maintenance. For explicitly structured data, interactions among these structures, access patterns and design of processing/access systems. Data administration, processing system life cycle, system security.

CSC 560 Software Engineering. (3 Hours) Formal approach to techniques and software design and development. Software cycle encompassed from initial ideas through code design and implementation with emphasis on object-oriented design techniques will be included. Software testing and maintenance will be discussed.

CSC 561 Graduate Capstone Project. (3 Hours) Prerequisite: Completion of 18 credits of graduate-level courses in Computer Science with a B grade or above. This course will provide students a comprehensive project development experience as part of a team. Students are expected to integrate their learning experiences into a coherent and focused project that effectively blends theory and practice. The focus of the projects could be on one or more of these: software design and development, modeling and simulation, data mining and quantitative analysis, and real-time performance studies. Students will periodically provide oral presentations on the progress of the project as well as submit written reports.

CSC 571 Programming for Big Data. (3 Hours) The course will expose students to three programming paradigms for big data analytics to cover the three Vs: Velocity, Volume, and Variety. The course will focus on design and development of programs based on the: (1) Supervised and unsupervised machine learning algorithms to perform predictive analytics of Big Data and implement them using a high-level interpreted language such as Octave; (2) Map-reduce parallel programming paradigm for selected data-intensive computational problems; (3) Functional programming paradigm using languages such as OCaml to analyze big data in a recursive fashion. In addition, the course will enable students to be able to configure a distributed file system based on the Hadoop architecture for reliable shared storage and develop programs that interface with it, as well as manage large datasets using SQL-like access to unstructured data (Hive) and NoSQL storage solutions (HBase).

CSC 573 Modeling and Simulation of Complex Systems. (3 Hours) The course focuses on the application of modeling and simulation principles to large-scale non-linear complex systems with interconnected parts (like a biological cell, economy or an ecological system). Topics covered include: non-linear differential equations, networks, stochastic models, cellular automata, agent-based modeling and swarm-like systems.

CSC 582 Social Network Analysis. (3 Hours) This course will cover the structure and analysis of large social networks on models and algorithms that abstract their properties. Topics covered include: Nodes, edges, and network measures, structure, and visualization and tools, the tie strength of networks, trust in social media, analyzing and classifying user roles, attributes and behavior, link prediction and entity resolution, epidemic models, location-based social media analysis, social sharing and filtering, aggregation and data mining, and network strategies for the individual and for the government.

CSC 595 Information Systems Development Project. (Variable 1-6 Hours) Prerequisites: Pass comprehensive examination and consent of advisor. Provide the student with the experience in analyzing, designing, implementing, and evaluating information systems. Students are assigned one or more system development projects. The project involves part or all of the system development cycle.

CSC 599 Thesis Research. (Variable 1-6 Hours) Prerequisites: Pass comprehensive examination and consent of advisor. An independent study course for the preparation of a Master’s thesis.

CSC 601 Computer Algorithms. (3 Hours) Prerequisite: CSC 515 Data Structures and Algorithm Analysis or CSC 323 Algorithm Design and Analysis or department approval. The course focuses on algorithms of different design strategies, and the mathematical concepts used in describing the complexity of an algorithm. Topics covered include: Asymptotic notations; Time complexity analysis of iterative and recursive algorithms; design strategies like Brute force, Divide and Conquer, Transform and Conquer, Greedy and Dynamic programming; Space-time tradeoffs in algorithms and NP-completeness – Heuristics and Approximation algorithms. The course will also cover graph theory algorithms and string matching algorithms with respect to the application of the above design strategies for specific problems.

CSC 620 Database Management Systems. (3 Hours) This course is designed for non-computer science majors entering the Ph.D. in Computational and Data Enabled Sciences and Engineering. It introduces students to the concepts and theories of database systems, necessary in the CDS&E fields. Topics include: information models and systems; the database environment; data modeling; conceptual modeling using the entity-relationship approach and mapping to relational tables; the relational model including the relational data structure, integrity rules, relational algebra and relational calculus; normalization; data definition and data manipulation in SQL; conceptual, logical, and physical database design; security; transaction management; query processing; and advanced topics in database systems, and how this applies to computational and data enabled sciences and engineering.

CSC 621 Machine Learning. (3 Hours) Pre-requisite: CSC 601 Computing Algorithms or CSC 515 Data Structures and Algorithm Analysis or CSC 323 Algorithm Design and Analysis. This course will deal enable students to understand the underlying algorithms used in various learning systems. Topics covered include: Inductive classification, Decision-tree learning, Ensembles, Experimental evaluation, Computational learning theory, Rule learning, Neural network learning, Support vector machines, Bayesian learning, Instance-based learning and Text categorization.

CSC 630 Computability and Complexity. (3 Hours) This course will cover advanced topics in computability and complexity theory. Computability topics covered include: Church-Turing Thesis, Decidability, Reducibility, Recursion Theorem and Decidability of logical theories. Complexity topics covered include: Time Complexity (P, NP, NP-Completeness), Space Complexity (Savitch’s theorem, PSPACE, NL-Completeness), Intractability, Probabilistic algorithms and Alternation.

CSC 634 Big Data Mining. (3 Hours) Pre-requisite: CSC 621 Machine Learning or department approval. This course will focus on data mining of very large amounts of data that is so large enough not to fit in main memory, characteristic of data retrieved from the web. Topics to be covered include: Distributed file systems and Map Reduce, Similarity search techniques, Real-time data-stream processing algorithms, Technology of search engines (PageRank, Link-spam detection, hubs-and-authorities approach) and Frequent-itemset mining. The course will also expose students to algorithms for clustering very large, high-dimensional datasets.

CSC 635 Big Data for Cyber Security. (3 Hours) Pre-requisite: CSC 621 Machine Learning or department approval. This course will focus on data-driven approaches to detect threats and attacks that originate from diverse channels at a rapid rate, necessitating the need for scalable distributed monitoring and cross-relation with a substantial amount of contextual information. The course will cover various anomaly-based Big Data analytics solutions for Cyber Security.

CSC 641 Network Science. (3 Hours) Pre-requisite: CSC 601 Computing Algorithms or CSC 515 Data Structures and Algorithm Analysis or CSC 323 Algorithm Design and Analysis. Topics covered include the measurement and structure of networks, methods for analyzing network data, including methods developed in physics, statistics, and sociology, graph theory, computer algorithms, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

CSC 651 Foundations of Programming and Computation Systems. (3 Hours) This course will focus on graduate-level central concepts in modern programming languages, impact on software development, language design trade-offs, and implementation considerations. Functional, imperative, and object-oriented paradigms. Formal semantic methods and program analysis. Modern type systems, higher order functions and closures, exceptions and continuations. Modularity, object-oriented languages, and concurrency. Runtime support for language features, interoperability, and security issues. Prerequisite: experience in any object-oriented language.

CSC 653 Large Scale Computing. (3 Hours) Prerequisite: CSC 551 Parallel and Distributed Computing. The course will focus on large-scale modeling techniques, algorithms and computational techniques for Big Data computing. Large-scale modeling techniques covered will include linear models, graphical models, matrix and tensor factorizations, clustering, and latent factor models. Algorithmic topics include sketching, fast n-body problems, random projections and hashing, large-scale online learning, and parallel learning. The computational techniques covered in this course will provide a basic foundation in large-scale programming, ranging from the basic “parfor” to parallel abstractions, such as MapReduce (Hadoop) and GraphLab.

CSC 661 Software Engineering for Computational Applications. (3 Hours) This course focuses on computational software engineering for engineering and scientific applications. Topics include Characteristics of computational software, Development and maintenance activities, Requirement engineering for computational software, Problem analysis and solution design tools, Component reuse, Software reliability, and Computational software validation and verification.

CSC 663 High Performance Scientific Computing. (3 Hours) The course will focus on design of high performance parallel programs for scientific computing. Topics covered include: Single-processor performance, memory hierarchy and pipelines; parallel system organization; message passing and MPI programming; Problem decomposition, graph partitioning, load balancing, Shared memory, CUDA, GPU and OpenMP programming.

Graduate Courses and Degree Options