### Definition of Prefixes

CAP – Computer Applications

CDA – Computer Design/Architecture

CEN – Computer Engineering Software

CGS – Computer General Studies

CIS – Computer and Information Systems (special topics)

CNT – Computer Networks

COP – Computer Programming (languages, data structures, software systems, operating systems, compiling)

COT – Computer Theory

### Special Course Offerings

There are various special courses offered every term. Special topics courses may evolve into regularly offered courses if they are successful, but they may also be once-only offerings. The latter is especially true if the enrollment of the first offering is low.

### 5000 Level Courses

**CAP 5137. Software Reverse Engineering and Malware Analysis (3).*** Prerequisite*: **CDA 3100**. This course covers fundamental problems, principles, and techniques in software reverse engineering of binaries including static analysis techniques, disassembly algorithms, dynamic analysis techniques, automated static and dynamic analysis techniques, malware analysis techniques, anti-analysis techniques, and malware obfuscation and packing techniques; many of the techniques will be demonstrated and practiced using IDA. The course also involves research opportunities to analyze new malware samples and firmwares and develop new analysis tools.

**CAP 5540. Bioinformatics: Sequence Analysis (3).**This is an interdisciplinary course between computer science and biology. Students do not have the prior knowledge of the algorithms and biology for taking this course. All algorithms and biology will be covered from scratch.

**CAP 5619. Deep and Reinforcement Learning Fundamentals (3).*** Prerequisite*: *Senior or grad standing in science or engineering; or instructor permission*. Requires some familiarity with basic concepts in linear algebra and probability theory, some basic knowledge of algorithm design, and programming experience with Python. This course covers fundamental principles and techniques in deep and reinforcement learning, as well as convolutional neural networks, recurrent and recursive neural networks, backpropagation algorithms, regularization and optimization techniques for training such networks, dynamic programming, Monte Carlo, and temporal difference, and function approximation reinforcement learning algorithms, and applications of deep and reinforcement learning. The course also covers active research topics in deep and reinforcement learning areas.

**CAP 5768. Introduction to Data Science (3).*** Prerequisite*: *Graduate standing in science or engineering, or instructor permission*. Students should be familiar with basic linear algebra concepts, probability theory, algorithm designs, and should have some Python or Java programming skills. This course is an introduction and overview of the fundamentals of Data Science. In this course, students become familiar with the Data Science process and how to use the methodologies and algorithms to approach real world problems.

**CAP 5769. Advanced Data Science (3).*** Prerequisite*: *COP 4530 (Computer Science undergraduate students); or IDC 4104 and graduate standing in science or engineering majors; or instructor permission*. Familiarity with basic linear algebra probability, algorithms, some Python or Java skills. This course is an intensive, advanced guide to Data Science. In this course, students become data scientists, capable of bother advanced data analysis and critical evaluation of the results.

**CAP 5778. Advanced Data Mining (3).*** Prerequisite*: *Students should have working knowledge of probability theory, linear algebra and common data mining algorithms; and should have taken a course covering the fundamentals of data structures, algorithms, and generic programing*. This course discusses advanced techniques for processing and mining large-scale digital data.

**CAP 5605. Artificial Intelligence (3).*** Prerequisite*: **COP 4530**. Introduction, representing knowledge, controlling attention, exploiting constraints, basic LISP programming, basic graph searching methods, game-playing and dealing with adversaries, understanding vision, theorem proving by computer, computer programs utilizing artificial intelligence techniques.

**CAP 5638. Pattern Recognition (3).*** Prerequisite*: *Knowledge of probability and at least one programming language*. Application of mathematical tools, in particular, probabilistic, algebraic, and linguistic tools, to problems in pattern recognition and classification. Feature selection procedures, syntactic pattern recognition. Applications of fuzzy set theory to pattern recognition and classification.

**CAP 5726. Introduction to Computer Graphics (3)**. *Prerequisite*: **COP 4530**. This course covers fundamental principles and algorithms underlying computer graphics, and also provides a brief introduction to openGL. The course is intended for computer-science graduate students who are interested in computer-graphics related careers or in learning and applying computer-graphics techniques.

**CDA 5125. Parallel and Distributed Systems (3).** *Prerequisite: COP 4610*. This course covers systems issues in parallel and distributed systems. Topics include parallel computer architectures, parallel system models, parallel programming paradigms, performance monitoring and optimization techniques, compilation techniques for parallel architectures, communication library implementations, and software/hardware fault tolerance techniques.

**CDA 5155. Computer Architecture (3).** *Prerequisite: CDA 3100*. Computer system components; microprocessor and minicomputer architecture; stack computers; parallel computers; overlap and pipeline processing; networks and protocols; performance evaluation; architecture studies of selected systems.

**CEN 5035. Software Engineering (3).** *Prerequisites: COP 4020, COP 4530, CEN 4021*. Survey of software engineering and detailed study of topics from requirement analysis and specification, programming methodology, software testing and validation, performance and design evaluation, software project management, and programming tools and standards.

**CEN 5526. Wireless & Mobile Computing (3).** *Prerequisites:* *None.* This course introduces students to the design, implementation, and analysis of mobile systems and applications in various domains, including urban sensing, mobile healthcare monitoring, security and privacy, location-aware services, and vehicular computing. Integral to the course will be the course projects in which students develop mobile applications on mobile devices. Through the course projects, students gain hands-on experience on building mobile applications and validate their research ideas in practice.

**CGS 5267. Principles of Computer Organization (3).** *Corequisites: COP 3330 and MAD 2104. * For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Basic computer structure and design, register transfer and micro operations, central processor organization, micro programming, arithmetic processor design, input-output, memory organization, virtual memory, microprocessors and microcomputer architecture.

**CGS 5268. Principles of Computer Organization II (3).** *Prequisite: CDA 3100*. For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Fundamental concepts in processor design, including datapath and control, pipelining, memory hierarchies, and I/O.

**CGS 5409. Object-oriented Programming in C++ for Non-majors (3).** *Prerequisite: COP 3014 -or a compatible course in C or C++ programming. Pre/co-requisite: COP 3353 * For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Topics include basics of C++ language, objects and classes, programming with classes, constructors and destructors, dynamic memory allocation, function and operator overloading, master classes, the class iostream, base and derived classes, and templates. May not be applied toward a degree in computer science.

**CGS 5425. Object-Oriented Programming with Data Structures (3). ** *Prerequisites: COP 3330, and MAD 2104. Pre/co-requisite: CDA 3100 *. For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Structured and object-oriented programming; invariant relations, stepwise refinement; text processing, internal sorting methods, linear tables, pointers and linked data structures, recursive programming and recursion elimination, sequential file processing; trees and graphs; program verification and running time analysis; application of concepts through programming projects.

**CGS 5426. Programming Language Concepts (3).** *Corequisite: COP 4530 *. For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. A survey of programming languages and language features and an introduction to compilers. Languages to be discussed include FORTRAN, Pascal, Ada, PL/1, APL, and LISP. An oral presentation is required.

**CGS 5427. Algorithm Design and Analysis (3).** * Prerequisites: COP 4530, MAD 3105-or-3107. Corequisites: STA4442, STA4321, or STA 3032*. For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Techniques for the analysis of computer algorithms; examples of well-designed algorithms and associated data structures; principles of algorithm design and application to programming projects.

**CGS 5428. Relational Database Theory (3). ** *Prerequisites: COP 3330 and MAD 2104*. For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Basic file organization methods, indexed files, multi-key processing; architecture of database management systems; relational, hierarchical network, and semantic database models; normalization; distributed databases and file systems; practical use of a DBMS and the building of a database application.

**CGS 5429. Introduction to Computer Theory (3).** *Prerequisites: MAD 3105.* For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Regular expressions; regular, context-free, context-sensitive, and unrestricted grammars; foundations of language theory; finite automata and linear grammars; pushdown automata; turing machines and non-solvability.

**CGS 5466. Programming for Non-Majors (3).** *Prerequisites: MAC 1140.* For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Fundamental concepts and skills of programming in a high-level language. Flow of control: sequence, selection, iteration, subprograms. Data structures: arrays, strings, structs, ADT lists and tables. Algorithms using selection and iteration (decision making, finding maxima and minima, basic searching and sorting, simulation, etc.). Good program design using a procedural paradigm, structure and style are emphasized. Interactive and file IO. Testing and debugging techniques.

**CGS 5765. Principles of Operating Systems (3).** *Prerequisites: CDA 3100, COP 4530.* For graduate non-majors and graduate majors needing foundational work in computer science; credit may not be applied towards a graduate degree in computer science. Design principles of batch and multi-programming and time-sharing operating systems. Linking, loading, input-output systems, interacting processes, storage management, process and resource control, file systems.

**CGS 5935. Special Topics in Computer Science for Non-Majors (1-3).** This course will offer special topics in Computer Science that are designed for non-Computer Science graduate students.

**CIS 5105. Computer Systems Performance Analysis (3).*** Prerequisite*: **COP 4610, MAD 3105, and STA 4442**. This course covers empirical, simulation, and analytical methods to evaluate computer systems. The emphasis is on the empirical methods. Through the course project, the students gain experience measuring and evaluating a system using proper experimental design, metrics, workloads, and statistical analysis techniques.

**CIS 5370. Computer Security (3).** *Prerequisites: COP 4610 or consent of instructor.* Covers threats and attacks (such as computer viruses and Trojan horses), access control, entity authentication, covert channels, inference and database security, secure operating systems, network security, legal and ethics aspects, administering security, physical security, and TEMPEST.

**CIS 5371 Cryptography (3).** *Prerequisites: MAD 3105. * This course addresses issues of modern cryptography covering theory and practice. Algorithms such as the RSA, EIGamal and the digital Signature Standard are covered in depth.

**CIS 5379. Computer Security Fundamentals for Data Science (3).*** Prerequisite*: **CGS 3465**. This course is an introduction to computer security, targeted towards graduate students in data science. This course covers a broad range of topics within computer security, such as cryptographic algorithms, security protocols, network authentication, and software security.

**CIS 5627. Introduction to Offensive Computer Security (3).*** Prerequisite*: **CDA 3100**. This course takes a hands-on approach to train students in the fundamental principles in computer security, including software security and web security. Its goal is to help students understand how various attacks work, what their fundamental causes are, how to defend against them, and how various defense mechanisms work. These key concepts are reinforced by various hands-on projects.

**CIS 5900r. Directed Individual Study (1-9).** (S/U grade only) May be repeated to a maximum of twelve (27) semester hours.

**CIS 5910r. Supervised Research (1-5).** (S/U grade only.) Cannot be applied to the master’s degree. May be repeated to a maximum of five (5) semester hours.

**CIS 5915r. Graduate Software Project(1-12).** (S/U grade only.) A minimum of six (6) semester hours of credit is required.

**CIS 5920r. Colloquium (1).** (S/U grade only.) Series of lectures given by faculty and visiting computer scientists. May be repeated up to a maximum of ten (10) semester hours.

**CIS 5930r. Selected Topics in Computer Science (1-3).** May be repeated to a maximum of twelve (12) semester hours.

**CIS 5935. Introductory Seminar on Research (3).**(S/U grade only). * Prerequisite: Admission to the MS or PhD in Computer Science degree program*. This seminar is a series of lectures given by faculty on the research being conducted by the Department of Computer Science. Other lectures include guidelines on the preparation of the doctoral portfolio, and on the use of library research tools.

**CIS 5940r. Supervised Teaching (1-5).** (S/U grade only.) May be repeated to a maximum of five (5) semester hours.

**CIS 5970r. Master’s Thesis (1-12).** (S/U grade only.) A minimum of nine (9) semester hours of credit is required.

**CIS 5949r. Internship in Computer Science (0–9).** (S/U grade only.) Prerequisite: **COP 4610**. This internship is a field placement in an approved industry or government entity having a significant information technology or computer science component. May be taken for variable credit and repeated with departmental approval. Credits do not count towards graduation. Successful completion requires satisfactory job evaluation and demonstration of educational value of placement via a paper. May be repeated to a maximum of thirty-six semester hours.

**CNT 5412. Network Security, Active and Passive Defenses (3). ** *Prerequisites: COP 4530, and MAD 2104, or consent of instructor.* Course covers defense of computer networks, investigation of threats to computer networks, network vulnerabilities, techniques for strengthening passive defenses, tools for establishing an active network defense, and policies for enhancing forensic analysis of crimes and attacks on computer networks.

**CNT 5505. Data and Computer Communications (3).** *Prerequisite: CDA 3100 and COP 4610*. Overview of networks; data communications principles; data link layer; routing in packet switched networks; flow and congestion control; multiple access communication protocols; local area network protocols and standards; network interconnection; transport protocols; integrated services digital networks (narrowband and broadband); switching techniques and fast packet switching.

**CNT 5529. Wireless Networking (3).** This course is intended to cover a wide spectrum of topics on wireless networks, including the physical layer, the medium access control layer, and the network layer. The focus is on understanding, implementing, and experimenting with various wireless networking technologies in different layers with software.

**CNT 5605. Computer and Network Administration (3).** *Prerequisite: COP 4610 *. UNIX user commands and shell programming. Problem solving and diagnostic methods, system startup and shutdown, device files and installing devices, disk drives and file systems, NFS, NIS, DNS, send mail. Managing a WWW site, managing UNIX software applications, system security, performance tuning. Legal and professional issues, ethics and policies.

**COP 5570. Concurrent, Parallel, and Distributed Programming (3).** *Prerequisite: COP 4610*. UNIX and C standards, file I/O, file access and attributes, directories, the standard I/O library, systems administration files, the process environment, process control, process relationships, signals, terminal I/O, daemon processes, interprocess communication, and pseudo terminals.

**COP 5611. Advanced Operating Systems (3).** *Prerequisites: CDA 3100, COP 4610, and introductory probability or statistics*. Design principles of batch, multiprogramming, and time-sharing systems; distributed systems; problems of concurrency.

**COP 5621. Compiler Construction (3).** *Prerequisites: CDA 3100, COP 4530, COT 4420 *. Introduction to compiling, elements of language theory, syntax-directed translation, lexical analysis, symbol tables, backtrack parsing, precedence parsing, LR(k) parsing, LL(k) parsing, intermediate code generation, code optimization, code generation, error detection and recovery.

**COP5659. Mobile Programming (3).** *Prerequisite: COP 4530*. This course teaches students how to program mobile devices. Students use event-based models to write and deploy an intent based application using a mobile computing software framework. May be repeated to a maximum of nine semester hours.

**COP 5725. Database Systems (3).** *Prerequisites: COP 4710, COP 4610*. Use of a generalized database management system; characteristics of database systems; hierarchical, network, and relational models; file organizations.

**COP 6622. Advanced Topics in Compilation (3).** *Prerequisite: COP 5621*. The course covers attribute grammars and attribute grammar processors, formal methods of semantic analysis, generalized tree transformers, code selection, analysis and optimization, as well as error analysis and recovery.

**COT 5310. Theory of Automata and Formal Languages (3).** *Prerequisites: COP 4020, COT 4420*. Formal models of computation; automata; formal languages, their relationships, decidable and undecidable problems.

**COT 5405. Advanced Algorithms (3). ** *Prerequisites: COP 4530.* Algorithms, formal proofs of correctness, and time complexity analysis for: network flow problems, approximation of NP hard combinatorial optimization problems, parallel algorithms, cache-aware algorithms, randomized algorithms, computational geometry, string algorithms, and other topics requiring advanced techniques for proof of correctness or time/space complexity analysis.

**COT 5410. Complexity of Algorithms (3).** *Prerequisites: COP 4530, and COT 4420*. Formal methods for the analysis of algorithm complexity, application to specific algorithms, lower bounds, asymptotically optimal algorithms, Reducibilities, NP completeness, and other classifications of “hard” problems.

**COT 5507. Analytical Methods in Computer Science (3).*** Prerequisite: COP 4530*. This course teaches computer science students the fundamental discrete mathematics required for serious graduate work in Algorithms and Theoretical Computer Science. It specifically covers topics in recurrent problems, sums, integer functions, elementary number theory, binomial coefficients, special numbers, and generating functions.

**COT 5715. Random Number Generation (3).** *Prerequisite: COT 4531*. This course provides a graduate level examination of all aspects of random number generation as used in simulation. Specifically, the course concentrates on pseudorandom number generation and quasirandom number generation theory and practice.

**ISC 5318. High Performance Computing (3).** *Prerequisites: COP 4020*. This course introduces high-performance computing, the use of parallel supercomputers, computer clusters, as well as software and hardware in order to speed up computations. Students learn to write faster code that is highly optimized for modern multi-core processors and clusters, using modern software-development tools and performance analyzers, specialized algorithms, parallelization strategies, and advanced parallel programming constructs.

**ISC5228. Monte Carlo Methods (3).** Prerequisites: * ISC 5305*,

*, and*

**MAC 2311***. This course provides an introduction to probabilistic modeling and Monte Carlo methods (MCMs) suitable for graduate students in science, technology, and engineering. It provides an introduction to discrete event simulation, MCMs and their probabilistic foundations, and the application of MCMs to various fields. In particular, Markov chain MCMs are introduced, as are the application of MCMs to problems in linear algebra and the solution of partial differential equations.*

**MAC 2312**### 6000 Level Courses

**CAP 6606. Fundamentals of Machine Learning Algorithms (3).** *Prerequisite: Familiarity with sets and logic, basic linear algebra, statistics, and calculus. Proficiency in a programming language, such as Python or C, at the level of COP 3014 or equivalent*. This course is a rigorous introduction to the design and analysis of machine learning algorithms, including algorithms for supervised, unsupervised, and reinforcement learning tasks. Students explore how bounds on the generalization ability of a given algorithm are formulated and proven. A variety of classical machine learning algorithms are analyzed in depth.

**CIS 6900r. Directed Individual Study (1-12).** (S/U grade only.) May be repeated to a maximum of twenty-four (24) semester hours.

**CIS 6930r. Advanced Topics in Computer Science (1-3).** May be repeated to a maximum of twelve (12) semester hours.

**CIS 6980r. Dissertation (1-12).** (S/U grade only.)

**COP 6622. Advanced Topics in Compilation (3).** *Prerequisite: COP 5621*. In-depth study of the following topics: attribute grammars and attribute grammar processors, formal methods of semantic analysis, generalized tree transformers, code selection, analysis and optimization, and error analysis and recovery.

### 8000 Level Courses

**CIS 8962r. Doctoral Qualifying Examination (0).** (P/F grade only)

**CIS 8964r. Doctoral Preliminary Examination (0).** (P/F grade only.)

**CIS 8966r. Master’s Comprehensive Examination (0).** (P/F grade only.)

**CIS 8974. Master’s Project Defense (0).** (P/F grade only.)

**CIS 8976r. Master’s Thesis Defense (0).** (P/F grade only.)

**CIS 8985r. Defense of Dissertation (0).** (P/F grade only.)

### Archived Courses

The following is a historic list of courses that are currently no longer offered. Archived courses may return based on department faculty interest.

**CAP 5415 Principles and Algorithms of Computer Vision (3).** *Prerequisites: COP 4530* This course covers the basic computational principles and algorithms to extract information from images and image sequences. Topics include imaging models, linear and non-linear filtering, edge detection, stereopsis and motion estimation, texture modelling, segmentation and grouping, and deformable matching for recognition.

**CDA 5140. Fault Tolerance and Reliability (3).** *Prerequisite: CDA 5155*. Basic definitions; self-checking circuits; error detection measures; interconnection networks; test generation and testability; distributed fault tolerance systems; software fault tolerance; fault tolerance and VLSI; error recovery.

**CEN 5000. Knowledge Management and Data Engineering (3).** *Prerequisite: COP 5710*. A survey of techniques and tools representing the transition from database management to knowledge management; database architecture and models; fuzzy databases; construction of knowledge bases.

**CEN 5055. Project Development (3).*** Prerequisites: CEN 5035.* This course deals with the planning, design, validation and implementation of a large scale project using IEEE deliverables, state-of-the-art software engineering techniques, analysis and design project reviews and evaluations prior to implementation in the Graduate Software Project.

** CEN 5064. Advanced Software Design (3). ***Prerequisite: CEN 5035.* This course concentrates on the design of software systems after requirements engineering has been completed. The course offers education in techniques such as architectural design, pattern integration, and re-factorings.

**CEN 5066. Software Engineering in Graphics (3).** *Prerequisite: CAP 4730*. Software engineering techniques as applied to graphical concepts based on ISO 7942, the Graphical Kernel Systems (GKS). Particular topics include binding times, concept coupling, segments, transformations, passive/active graphics, clipping. A class project is required.

**CEN 5720. Computer-Human Interaction (3).** *Prerequisite: COP 4530*. Systematic analysis of user needs and activities from the point of view of the actual user. Design and implementation of effective, user-friendly software. Methods of analysis. Performance and interface of programs. User anxiety and convenience.

**CNT 5415. Applied Computer and Network Security (3).** In this course, students familiarize themselves with current and emerging threats to the security of computer systems and networks, including viruses, worms, and network intrusion; and with techniques for the prevention, detection, and recovery from such attacks, such as firewalls, intrusion detection systems, secure coding practices, and others. Attack and defense mechanisms are studied in a systematic way to develop students’ practical and analytical skills to identify and correct or mitigate threats to computer systems and networks.

**COP 5385. HSM and Reactive Systems (3).** *Prerequisites: COP 4530, COP 4610*

**.**Hierarchical state machines (HSM) are finite state machines with a behavioral inheritance hierarchy. HSM provide a theoretical model for event-driven (reactive) systems. The course studies this HSM model and introduces a framework for implementing reactive systems based on HSM models of systems. The use of HSM as an organizing principle for applications software, from desktop to real time, is studied. A range of applications is discussed and a student project is required.

**COP 5517. Generic Programming (3).** *Prerequisite: COP 4530.* Generic Programming Principles and Techniques, including most of the following topics: Generic Containers; Function and Predicate Objects; Generic Algorithms; Mediation between containers and algorithms with iterators; Containers and Algorithms in the C++ STL Vectors, Lists, and Deques; Stacks, Queues, Priority Queues, Ordered Sets and Maps, Hashed Sets and Maps, Iterators and Iterator Adaptors, General algorithms, Set algorithms, Heap algorithms, Search algorithms, and Sort algorithms; Extending the STL; Graphs, Digraphs, and Graph Algorithms; Policy Based Design; Partial template specialization; Traits; Typelists; Design Pattern Implementations: Singletons; Smart Pointers; Abstract Factory; and related special topics.

**COP 5641. Kernel and Device Driver Programming (3).** *Prerequisite: COP 4610, COP 5570, or permission of instructor.* Internals of the Linux operating system kernel, including virtual and physical memory management, scheduling, and device drivers. Kernel modules, hardware interfaces, char and block devices, kernel debugging, interrupt handling, memory mapping. Laboratory exercises modifying example modules, project developing a new device driver.

**COP 5642. RealTime Systems Theory and Practice (3).** *Prerequisite: COP4610 or COP 5570*. Theoretical foundations and practical techniques for the design and implementation of real time computer systems. Topics include applicable scheduling theory, the use of computers for controlling real time processes, the use of a real-time operating system. Laboratory work includes writing software to control a physical device with hard timing constraints, and analysis of scheduling performance by simulation. A term project is required.

**COP 5818. Distributed Applications Development (3).** * Prerequisite: COP 3252*. Programming of distributed web applications using Java database connectivity, servlets, Java server pages, remote method invocation, and enterprise Java beans; use of the Sun Microsystems Java 2 Enterprise Edition development platform either directly or through an integrated development environment such as IBM’s websphere.

**COT 5315. Programming Language Foundations (3).** *Prerequisites: COP 4020, and MAD 3105 *. Conceptual subtleties in programming languages; formal specification of syntax and semantics; issues in the design and implementation of programming languages.

**COT 5540. Logic for Computer Science (3).** *Prerequisite: COT 4420*. Syntax, semantics, and proof theory of propositional logic and first order languages; prenex normal form; Gentzen systems; resolution for propositional logic; elements of PROLOG and program verification.

**CAP 6417. Theorectical Foundations of Computer Vision (3).*** Prerequisite: CAP 5415.* This course covers theoretical foundations of computer vision. By formulating vision as an inference process, approaches to vision are presented and analyzed systematically. Topics include Marr’s computational vision paradigm, regularization theory, Bayesian inference framework, pattern theory, and visual learning theories.

**CAP 6606. OFFENSIVE NETWORK SECURITY (3).** This course provides comprehensive coverage of fundamental problems, principles, techniques, and commonly used tools for offensive network security. The course also covers real world policy (legal) and implementation issues in network penetration testing.

**CIS 6935r. Advanced Seminar in Computer Science**(S/U grade only) (1). May be repeated to a maximum of eight (12) semester hours.