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Topics include security and cryptography basics, vulnerability analysis, secure software development, and distributed system security. Projects involve designing and programming basic security tools, secure programs, and distributed systems.

CS Security Engineering. Covers the software subsystems that are involved in defending computer systems. Studies threats and architecting solutions against them, including but not limited to access control and identity management, network and system security, intrusion detection and recovery systems, monitoring and forensic systems.

CS Operating Systems. Issues in multiprogramming. CS Concurrent and Distributed Systems. Practical issues in designing and implementing concurrent and distributed software. Topics include concurrent programming, synchronization, multithreading, local and wide-area network protocols, distributed computation, systems integration, and techniques for expressing coarsegrained parallelism at the application level. Projects involve network programming at application level. CS Mobile Application Development.

This project based course will teach fundamental principles of software development for the mobile device environment, emphasizing the application of numerous academic concepts and the new design and programming paradigms that stem from the use of mobile devices.

Topics include user interfaces, event-based programming, interprocess communications, networking, mobile-specific capabilities and performance in a resource restricted environment. CS Introduction to Artificial Intelligence. Principles and methods for knowledge representation, reasoning, learning, problem solving, planning, heuristic search, and natural language processing and their application to building intelligent systems in a variety of domains.

CS Computer Vision. Basic principles of visual perception and their implementation on computer systems. Topics include early visual processing, edge detection, segmentation, intrinsic images, image modeling, representation of visual knowledge, and image understanding. Students complete projects involving real images. CS Analysis of Algorithms. Analyzes computational resources for important problem types by alternative algorithms and their associated data structures, using mathematically rigorous techniques.

Specific algorithms analyzed and improved. CS Data Mining. Basic principles and methods for data analysis and knowledge discovery.

Emphasizes developing basic skills for modeling and prediction and performance evaluation. Topics include system design; data quality, preprocessing, and association; event classification; clustering; biometrics; business intelligence; and mining complex types of data. CS Autonomous Robotics. Covers various basic topics in autonomous robotics, such as autonomous architectures and their interaction with physical hardware, elementary kinematics and robot control, motion and trajectory planning, localization, task planning, learning and adaptation, modeling, and sensor fusion.

Includes projects involving physical robots. CS Introduction to Cryptography. Covers formal definitions of security for the most common tasks: data encryption and authentication, in both the private key and public key settings. Covers the process of formally proving that constructions meet the appropriate security definitions. Also covers practical constructions and applications, such as how to correctly use block ciphers and hash functions for the tasks above.

In addition, several current topics in cryptography may also be covered. CS Design Exhibition. Capstone course focusing on design and successful implementation of major software project, encompassing broad spectrum of knowledge and skills, developed by team of students.

Requires final exhibition to faculty-industry panel. May be repeated within the degree for a maximum 12 credits. Recommended Prerequisite: Two other CS level courses; and senior standing. Senior design project course focusing on design and successful implementation of major software project specified by an industry sponsor, encompassing broad spectrum of knowledge and skills, developed by team of students.

Three credits may count towards the Senior Computer Science requirements, and the second three credits may count towards Computer Science-Related requirements. May be repeated within the degree for a maximum 6 credits. Research and analysis of selected problems or topics in computer science. Topic must be arranged with instructor and approved by department chair before registering. Notes: May be repeated if topics substantially different.

Topics of special interest to undergraduates. May be repeated within the term for a maximum 24 credits. Recommended Prerequisite: 60 credits. Additional prerequisites may vary with nature of topic. Select contemporary topics in Engineering and Computing. Students in a Non-Degree Undergraduate degree may not enroll. Grading: This course is graded on the Graduate Special scale. Techniques to store, manage, and use data including databases, relational model, schemas, queries and transactions.

Overview of Data Mining principles, models, supervised and unsupervised learning, pattern finding. Massively parallel architectures and Hadoop. May not be repeated for credit. Enrollment is limited to Graduate, Non-Degree or Undergraduate level students. Enrollment limited to students in the Volgenau School of Engineering college. Grading: This course is graded on the Graduate Regular scale. This course focuses on the topics of basic mathematical structures, mathematical logic and probability theory; and application of these concepts to problem solving and formal reasoning through hand-on practice with the use of computational tools.

May only be taken in the first semester at GMU. This course is a hands-on introduction to the systems level of programming with an emphasis on data structures and interfacing with operating systems.

This course focuses on fundamental data structures needed to design and implement systems applications and continues with an introduction to the Unix Application Programming Interface, signals, threads, and interprocess communications. This course is taught from a programmatic perspective using C, with special topics in both Java and Python. CS Language Processors. Basic programming language processors such as assemblers, interpreters, and compilers. Topics include design and construction of language processors, formal syntactic definition methods, parsing techniques, and code generation techniques.

Lab includes construction of language processors and experience with programming environments. CS Database Systems. An introduction to database management with focus on architecting databases and using them in applications. Topics to be covered include: data modeling with the Entity-Relationship model, the relational data model and its formal languages, SQL, the theory of database design, object databases, XML and Web data.

Graphics principles and programming. Topics include graphics hardware, antialiasing, transformations, viewing, illumination, blending, texture mapping, color models, curves, surfaces, and animation. Techniques and systems for communication of data between computational devices and layers of Internet Protocol Suite.

Topics include role of various media and software components, local and wide area network protocols, network design, performance and cost considerations, and emerging advanced commercial technologies. Models of operating systems. Artificial Intelligence principles and methods. Topics will include uninformed search, informed search, adversarial search, probabilistic reasoning and models, Bayes networks, machine learning fundamentals, classification and clustering, and neural networks.

Additional topics may include knowledge representation, constraint satisfaction search, agent architectures, and Markov decision problems, among others.

Topics include analyzing sequential and parallel algorithmic strategies such as greedy methods, divide and conquer strategies, dynamic programming, search and traversal techniques, and approximation algorithms; and analyzing specific algorithms falling into these classes, NP-Hard and NP-Complete problems.

Concepts and techniques in data mining and multidisciplinary applications. Topics include databases; data cleaning and transformation; concept description; association and correlation rules; data classification and predictive modeling; performance analysis and scalability; data mining in advanced database systems, including text, audio, and images; and emerging themes and future challenges.

Recommended Prerequisite: CS Special topics in computer science not occurring in existing courses. May be repeated within the degree for a maximum 9 credits. Introduction to logic and proof techniques, formal languages, automata theory, and computational complexity.

Specific topics include regular and context-free languages, Turing machines, NP-completeness, and undecidability. CS Advanced Algorithms. Provides an overview of advanced algorithm design and analysis techniques. Topics include algorithms for hash tables, matrix operations, number theory, string matching, computational geometry, combinatorial optimization, and linear programming; also the areas of NP-completeness and approximation algorithms.

CS Computational Geometry. Basic principles and methods for computing in field of geometric modeling. Emphasizes data structures used to represent geometric objects and algorithms for manipulating those data structures. Topics include range searching, polygon triangulation, convex hulls, motion-planning, visibility, and mesh generation. CS Foundations of Parallel Computation. Emphasizes interfaces between algorithm design and implementation, architecture, and software.

Examines parallel algorithms and parallel programming languages relative to architecture of particular parallel computers. Recommended Prerequisite: Proficiency in C programming language. Choose Advanced system settings , Performance. Uncheck all boxes in the list. This way your operating system will not look up to date but performance will be much higher.

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