CS

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Hours or CS 126G Literacy and Research for or OEAS 130G Scientists or Honors: Introduction to

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

and Research

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

or CS 151 Introduction to Programming with Java 6 or CS 153 Introduction to Programming with Python

Subject

CS

Credits (min)

4

Credits (max)

4

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

or

Subject

CS

Credits (min)

8

Credits (max)

8

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

or 8 CS 150 Introduction to Programming with C++

Subject

CS

Credits (min)

8

Credits (max)

8

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

3 CS 250 Programming with C++ * 4 or CS 251 Programming with Java

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Special topics in computer science that are not part of the current curriculum at the freshman/sophomore level.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Subject

CS

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course provides an in-depth introduction to information literacy from library and information science, information ethics, and computer science perspectives along with applications to cybersecurity research and professional activity. This course is aligned with Old Dominion University’s general education learning outcomes for information literacy.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

ENGL 110C

This course introduces the basic concepts and algorithms of digital image processing. Topics include image representation, sampling, quantization, enhancement, filtering, restoration, segmentation, color image processing, imaging geometry, image transforms, and morphological processing.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

or CS 251 or Programming with Java

Subject

CS

Credits (min)

4

Credits (max)

4

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

& CS 260 and C++ for Programmers 4 or 8 CS 253 Transfer Credit for Programming with & CS 260 Python & CS 261 and C++ for Programmers and Java for Programmers or

Subject

CS

Credits (min)

8

Credits (max)

8

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Available for pass/fail grading only. An introduction to Unix with emphasis on the skills necessary to be a productive programmer in Unix, Linux, and related environments. Topics include SSH, command line shells, files and directories, editing, compiling and debugging, SSH keys, git and programming IDEs. CS 153, ENGN 122, DASC 257, or IT 205

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any one of: CS 150, CS 151,

& CS 260 Python & CS 261 and C++ for Programmers and Java for Programmers or

Subject

CS

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

0-6 or CS 261 Java for Programmers 3 CS 270 Introduction to Computer Architecture II 3

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. An introduction to the Java programming language for students who are familiar with programming in C++ or Python. Topics include basic language syntax, data structures, control flow, classes, inheritance, exception handling, and basic elements of the Java API. Not open to students with credit for CS 251. ECE 250

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any one of: CS 250, CS 253 or

Laboratory work required. An introduction to the Python programming language for students who are familiar with programming in C++ or Java. Topics include basic language syntax, data structures, control flow, classes, inheritance, and basic elements of the Python standard library. Not open to students with credit for CS 253. ECE 250

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any of: CS 250, CS 251 or

Fundamentals of the architecture and operation of modern computers. Building an ALU. The cache-Ram interaction. The virtual memory system. The Fetch/Execute cycle. Implementing a set of the ALU, Load/ Store and Branch instructions in a single cycle implementation. Basics of microprogramming. Design of the control unit. A pipelined implementation. Multicores, multiprocessors and clusters.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 170

Special topics in computer science which are not part of the current curriculum at the freshman/sophomore level.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Covers changes in the world's society due to continuing implementation of computing technologies. Evaluation of technological expansions in areas of governments, business/industry, education, medicine, transportation, communication and entertainment. Topics include: intellectual property, software piracy, computer crimes and ethics. Students must research a societal topic and present in written and oral forms.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

ENGL 110C

Laboratory work required. An in-depth introduction to the Internet and the World Wide Web for CS or similar majors as a basis for more advanced studies in Web programming. Topics include: historical and current development of the Internet Web document publishing. Internet design, communication, and application protocols and the tools that use them. Internet search tools and their design. Internet issues such as netiquette, copyright, spam, computer viruses, cookies, security, and future of the Internet.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 252

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. The techniques, idioms, and design patterns of object-oriented programming. Methods of object-oriented analysis and design with the Unified Modeling Language. Multi-thread programs, synchronization, and graphic user interfaces. CS 253, ECE 250

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 252 and a grade of C or better in any of: CS 250, CS 251,

or CS 330 Engineering or Object-Oriented Design and Programming Technical Elective *** 3

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Survey of significant features of programming languages. Language types including imperative, functional, logical, and object-oriented are covered. Concepts include lexical and syntactic analysis, type systems, flow control, modularity, and parallel programming. Small programs in several languages required. Laboratory work required. CS 253, ECE 250

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 252 and a grade of C or better in any of: CS 250, CS 251,

8 CS 390 Introduction to Theoretical Computer 3

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Available for pass/fail grading only. Student participation for credit based on the academic relevance of the work experience, criteria, and evaluative procedures as formally determined by the department prior to the semester in which the work experience is to take place. Written report required. be provided by the Monarch Internship and Co-Op Office in the semester prior to enrollment

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

Approval by department is required; Additional support may

Available for pass/fail grading only. Academic requirements will be established by the department and will vary with the amount of credit desired. Allows students to gain short duration career-related experience. An academic project may be required by the department to enhance the value of the educational experience. Written report required. be provided by the Monarch Internship and Co-Op Office in the semester prior to enrollment

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

Approval by department is required; additional support may

3 ECE 241 Fundamentals of Computer Engineering 4

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Special topics in computer science that are not part of the current curriculum at the junior/senior level.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

Laboratory work required. Foundational principles and techniques for building correct-by-construction software systems with provable guarantees. Includes functional programming, algebraic and polymorphic data types, pattern matching, computer-assisted theorem proving, proof automation, extraction of certified executable code, examples of verified algorithms.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 381

Laboratory work required. Provides students with challenges of business environments in developing a technology based project. Students identify a societal problem, identify solutions, define project solutions, develop project objectives, conduct feasibility analysis, establish organizational group structure to meet project objectives and develop formal specifications. Students make formal technical project presentations and develop web documentation. Students prepare a draft grant proposal. Pre- or corequisite: CS 350

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 330 or CS 361

Development II (Grade of C or better required to meet the

Subject

CS

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Algorithms and software for fundamental problems in scientific computing. Topics: properties of floating point arithmetic, linear systems of equations, matrix factorizations, stability of algorithms, conditioning of problems, least-squares problems, eigenvalue computations, numerical integration and differentiation, nonlinear equations, iterative solution of linear systems. CS 251, CS 253, ECE 250

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

MATH 316 and a grade of C or better in any of: CS 250,

Select two of the following: 6

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

PHIL/AI 446 Artificial Intelligence (AI) Ethics and Policy 3 CYSE/AI 410 Artificial Intelligence (AI) Methods and 3

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Extensive coverage of the hypertext transfer protocol (HTTP), specifications and commentary (IETF RFCs), and implications for servers and clients. Students will develop a web server providing common HTTP functionality and implementing all HTTP (including unsafe and conditional) methods, content negotiation, transfer and content encoding, basic & digest authentication, and server-side execution of programs (i.e., dynamic resources). Frequent in-class demonstrations of progress and protocol conformance will be required. CS 153

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any one of: CS 150, CS 151,

Provides an overview of the World Wide Web and associated decentralized information structures, focusing mainly on the computing aspects of the Web: how it works, how it is used, and how it can be analyzed. Students will examine a number of topics including: web architecture, web characterization and analysis, web archiving, Web 2.0, social networks, collective intelligence, search engines, web mining, information diffusion on the web, and the Semantic Web. CS 253, CS 260, CS 261, CS 263, or DASC 255

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any one of: CS 250, CS 251,

Provides detailed experience with: principles of web security, attacks and countermeasures, the browser security model, web app vulnerabilities, injection, denial-of-service, TLS attacks, privacy, fingerprinting, same-origin policy, cross site scripting, authentication, JavaScript security, emerging threats, defense-in-depth, techniques for writing secure code, web archiving, and rehosting.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 312 and CS 330

Laboratory work required. Project-oriented coverage of the principles of application design and development for Android platform smart devices. Topics include user interface; input methods; data handling; network techniques; localization and sensing. Students are required to produce a professional-quality mobile application. a CS 445/545 Introduction to Quantum Computing (3 Credit Hours) The course covers the fundamental concepts of quantum information and computation, including the quantum bits, single- and multi-gate circuits, and related concepts like superposition, entanglement, interference, measurements, basic theory, quantum communication protocols, and time- permitted basic algorithms in quantum computing. The class focuses on the material required to transition from theory to practical use cases based on the class's interests. Familiarity with Jupyter Notebooks, linear algebra, and discrete structures is recommended. CS 261 or CS 263

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 330 and either CS 251 or CS 261 A grade of C or better in CS 150, CS 151, CS 153, CS 260,

Laboratory work required. Three level database architecture. The relational database model and relational algebra. SQL and its use in database procedures and with conventional programming languages. Entity relationship modeling. Functional dependencies and normalization. Transactions, concurrency and recovery. CS 330 or CS 361

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 252 and a grade of C or better in CS 381 and either

Laboratory work required. The administration of computer networks and their interaction with wide area networks: network topologies for local and wide area networks, common protocols and services, management of distributed file services, routing and configuration, security, monitoring and trouble-shooting.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 455

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Aspects of administering a SOLARIS/UNIX operating system in a networked environment are covered. Topics covered include installation, file system management, backup procedures, process control, user administration, device management, Network File Systems (NFS), Network Information Systems (NIS), UNIX security, Domain Name Services (DNS), and integration with other operating systems.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

experience with UNIX

Laboratory work required. An introduction to graphical systems and methods. Topics include basic primitives, windowing, transformations, hardware, interaction devices, 3-D graphics, curved surfaces, solids, and realism techniques such as visible surface, lighting, shadows, and surface detail. Requires project involving OpenGL programming.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 361

or ECE/MSIM Foundations of Cyber Security 470

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course covers mathematical foundations, including information theory, number theory, factoring, and prime number generation; cryptographic protocols, including basic building blocks and protocols; cryptographic techniques, including key generation and key management, and applications; and cryptographic algorithms--DES, AES, stream ciphers, hash functions, digital signatures, etc.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

MATH 162M

Authentication in cyber systems including password-based, address-based, biometrics-based, and SSO systems; Authorization and accounting in cyber systems; Securing wired and wireless networks; Secured applications including secure e-mail services, secure web services, and secure e- commerce applications; Security and privacy in cloud environments.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

MATH 162M

Total Credit Hours 12 * CS 252 is a prerequisite and is not included in the calculation of the grade point average for the minor. A grade of C or better is required in any of these courses if they are used as a prerequisite to any other CS course. Students must have a minimum overall cumulative grade point average of 2.00 in all courses specified as a requirement for the minor exclusive of 100- and 200-level courses and prerequisite courses and complete a minimum of six hours in upper-level courses in the minor through courses offered by Old Dominion University.

Subject

CS

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

This course is designed to help students gain a thorough understanding s of vulnerabilities and attacks in systems and networks and learn cyber rt defense best practices. It covers fundamental security design principles and defense strategies and security tools used to mitigate various cyber attacks. The topics may include identification of Recon Ops, intrusion detection, identification of C2 Ops, data exfiltration detection, identifying malicious codes, network security techniques, cryptography, malicious activity detection, system security architectures, defense in depth, distributed/cloud and virtualization. CS 253, CS 270, CS 455, CS 462, CS 471, or ECE 250; no prior knowledge of computer security is necessary

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any one of: CS 250, CS 251,

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

The course introduces classical and advanced models and techniques in machine learning and deep learning. It applies these techniques in the cybersecurity domain including anomaly detection, network security, and malware detection and classification. Advanced applications such as self- driving cars and IoT systems are also discussed. In addition, cyber-attacks on machine learning techniques and AI systems and the possible consequences are also discussed.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

CS 462 or CS 455 or experience in cybersecurity

This course provides an overview of the fundamentals of computer architecture. Major components include basic computer logic, integrated circuits, instruction sets, cache-RAM interaction, virtual memory, the fetch/ execute cycle, basics of microprogramming, pipelined implementation, parallel processors including SISD, MIMD, SIMD, and SPMD. The course also introduces hardware multithreading, multicores, multiprocessors, GPUs, multiprocessor network topologies, and cluster networking.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 170

Technical Elective *** 3 Human Behavior Way of Knowing 3 Total Credit Hours 122 * Does not include the University's General Education language and culture requirement. Additional hours may be required. ** CHEM 120 is for online program students only. *** Computer Engineering major students need three technical elective courses selected from one of three options: (1) three 400-level ECE technical elective courses; (2) two 400-level ECE technical elective courses and one 300- level ECE technical elective course or one approved 300- or 400-level CS/MATH/Engineering course; (3) two 400- level ECE technical elective courses and one approved 300- or 400-level CS course or one approved 300- or 400- level CS/MATH/Engineering course. 579 Computer Engineering (BSCE)

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Basic protocols, techniques and programming issues to secure network and computer systems. Topics include: cryptographic algorithms and concepts (Secret Key Cryptography, Hashes and Message Digests, Public Key and Authentication); Security Standards (Kerberos, Public Key Infrastructure, IPsec, SSL/TLS); Security applications (PEM, S/MIME, PGP, HTTP, Firewalls); Hands-on programming using OpenSSL.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 361

Laboratory work required. Efficient implementation methods. Time management. Planning and design of simulation experiments. Statistical issues in simulation. Generation of random numbers and stochastic variates. Programming with graphically- and text-based simulation languages. Verification and validation of simulation models. Distributed simulation. Special topics such as HLA will be discussed.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

STAT 330 and a grade of C or better in CS 330 or CS 361

Laboratory work required. This course is to help students fully understand and utilize the internal workings and capabilities provided by modern computing, networking and programming environments. Topics include: Shell Script Programming, X Windows (Xlib and Motif), UNIX internals (I/O, Processes, Threads, IPC and Signals), Network Programming (UDP/ TCP Sockets and Multicasting) and Java Systems Programming (SWING, Multithreading and Networking).

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 330 and CS 361

Subject

CS

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Introduction to concepts, principles, challenges, and research in major areas of AI. Areas of discussion include: natural language and vision processing, machine learning, machine logic and reasoning, robotics, expert and mundane systems. CS 253, CS 260, CS 261, CS 263, or DASC 255

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in any one of: CS 250, CS 251,

This course offers a comprehensive, project-driven approach to integrating AI in healthcare, focusing on building prototype models for prevention, diagnosis, and treatment while stressing the need of adopting ethical, responsible methods to reduce risks and improve patient outcomes.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

Instructor permission required

Laboratory work required. The motivation for and successes of parallel computing. A taxonomy of commercially available parallel computers. Strategies for parallel decompositions. Parallel performance metrics. Paralle algorithms and their relation to corresponding serial algorithms. Numerous examples from scientific computing, mainly in linear algebra and differential equations. Implementations using public-domain network libraries on workstation clusters and computers.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

MATH 316; knowledge of a high level language

Laboratory work required. Fundamental concepts of parallel computing: Machine models, architectures, parallel topologies and languages, parallel algorithm design and parallel programming, architecture independent message passing interface (MPI) communication library, and scaled- speedup. Group project required. CS 330; CS 417 or linear algebra is recommended 855 CSD - Communication Sciences and Disorders

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 270 and either CS 361 or

Laboratory work required. Theoretical and practical aspects of compiler design and implementation. Topics will include lexical analysis, parsing, translation, code generation, optimization, and error handling.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 361

Laboratory work required. Students perform mentored research in a group environment to develop computational approaches in addressing computer science challenges. The project needs approval by the Computer Science Honors Program director, and registration requires approval of the mentor. A GPA of 3.00 or better is required, or approval by the director of the Computer Science Honors Program.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

grade of C or better in CS 350

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Special topics.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

717 Computer Science (BSCS) C. Contract Honors Designation for Upper-Division Computer Science courses Students with a grade point average of at least 3.25 may convert any upper- division computer science course into an Honors course on an individual basis. No grade below B is accepted for Honors designation. An Honors designation of a course requires successful completion of honors-level tasks to be agreed upon by the student and the instructor. Students who plan to apply for the honors designation of a course are required to communicate with the instructor before registration. Students are required to submit an outline of honors work to Honors Program Coordinator and obtain an approval before the start of the semester in which the course is taken.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

The course aims to provide students foundational training in computing. This includes topics in discrete mathematics, counting and combinatorics, probability, proofs methods, basic automata theory and algorithm design and analysis. equivalent experience with programming and basic data structures

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

MATH 211 or equivalent, CS 250 or CS 251 or CS 253 or

Laboratory work required. Foundational principles and techniques for building correct-by-construction software systems with provable guarantees. Includes functional programming, algebraic and polymorphic data types, pattern matching, computer-assisted theorem proving, proof automation, extraction of certified executable code, examples of verified algorithms.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 381 or equivalent experience

Laboratory work required. Provides students with challenges of business environments in developing a technology based project. Students identify a societal problem, identify solutions, define project solutions, develop project objectives, conduct feasibility analysis, establish organizational group structure to meet project objectives and develop formal specifications. Students make formal technical project presentations and develop web documentation. Students prepare a draft grant proposal.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Students write professional documents and continue the development of the project defined in CS 410/510. Written work is reviewed and returned for corrective rewriting. Students will design and develop a project prototype using accepted best practices of professional software development. They will demonstrate the prototype to a formal panel and defend its satisfaction of the goals established in CS 410/510. This is a writing intensive course.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Algorithms and software for fundamental problems in scientific computing. Topics: properties of floating point arithmetic, linear systems of equations, matrix factorizations, stability of algorithms, conditioning of problems, least-squares problems, eigenvalue computations, numerical integration and differentiation, nonlinear equations, iterative solution of linear systems.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Overview of Internet and World Wide Web; web servers and security, HTTP protocol; web application and design; server side scripts and database integration, and programming for the Web. experience

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 312 and CS 330, or equivalent

or CS 580 Introduction to Artificial Intelligence

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. Extensive coverage of the hypertext transfer protocol (HTTP), specifications and commentary (IETF RFCs), and implications for servers and clients. Students will develop a web server providing common HTTP functionality and implementing all HTTP (including unsafe and conditional) methods, content negotiation, transfer and content encoding, basic & digest authentication, and server-side execution of programs (i.e., dynamic resources). Frequent in-class demonstrations of progress and protocol conformance will be required. programming

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Familiarity with Internet and network (including socket)

An overview of the World Wide Web and associated decentralized information structures, focusing mainly on the computing aspects of the Web: how it works, how it is used, and how it can be analyzed. Students will examine a number of topics including: web architecture, web characterization and analysis, web archiving, Web 2.0, social networks, collective intelligence, search engines, web mining, information diffusion on the web, and the Semantic Web.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Provides detailed experience with: principles of web security, attacks and countermeasures, the browser security model, web app vulnerabilities, injection, denial-of-service, TLS attacks, privacy, fingerprinting, same-origin policy, cross site scripting, authentication, JavaScript security, emerging threats, defense-in-depth, techniques for writing secure code, web archiving, and rehosting.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

A grade of C or better in CS 312 and CS 330

Laboratory work required. Project-oriented coverage of the principles of application design and development for Android platform smart devices. Topics include user interface; input methods; data handling; network techniques; localization and sensing. Students are required to produce a professional-quality mobile application.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of Java

The course covers the fundamental concepts of quantum information and computation, including the quantum bits, single- and multi-gate circuits, and related concepts like superposition, entanglement, interference, measurements, basic theory, quantum communication protocols, and time- permitted basic algorithms in quantum computing. The class focuses on the material required to transition from theory to practical use cases based on the class's interests. Familiarity with Jupyter Notebooks, linear algebra, and discrete structures is recommended.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience, with preference for Python

Choose three from the following:* 9

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-undergraduate

Source

catalog.odu.edu

Laboratory work required. The administration of computer networks and their interaction with wide area networks: network topologies for local and wide area networks, common protocols and services, management of distributed file services, routing and configuration, security, monitoring and trouble-shooting.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Aspects of administering a SOLARIS/UNIX operating system in a networked environment are covered. Topics covered include installation, file system management, backup procedures, process control, user administration, device management, Network File Systems (NFS), Network Information Systems (NIS), UNIX security, Domain Name Services (DNS), and integration with other operating systems.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

experience with UNIX

Laboratory work required. An introduction to graphical systems and methods. Topics include basic primitives, windowing, transformations, hardware, interaction devices, 3-D graphics, curved surfaces, solids, and realism techniques such as visible surface, lighting, shadows, and surface detail. Requires project involving OpenGL programming.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Introduction to networking and the Internet protocol stack; Vulnerable protocols such as HTTP, DNS, and BGP; Overview of wireless communications, vulnerabilities, and security protocols; Introduction to cryptography; Discussion of cyber threats and defenses; Firewalls and IDS/ IPS; Kerberos; Transport Layer Security, including certificates; Network Layer Security. Old Dominion University Graduate Catalog 2025-2026 472

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course covers mathematical foundations, including information theory, number theory, factoring, and prime number generation; cryptographic protocols, including basic building blocks and protocols; cryptographic techniques, including key generation and key management, and applications; and cryptographic algorithms--DES, AES, stream ciphers, hash functions, digital signatures, etc.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

MATH 162M

Authentication in cyber systems including password-based, address-based, biometrics-based, and SSO systems; Authorization and accounting in cyber systems; Securing wired and wireless networks; Secured applications including secure e-mail services, secure web services, and secure e- commerce applications; Security and privacy in cloud environments.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Introduction to information assurance. Topics to be covered include metrics, planning and deployment; identity and trust technologies; verification and evaluation, and incident response; human factors; regulation, policy languages, and enforcement; legal, ethical, and social implications; privacy and security trade-offs; system survivability; intrusion detection; and fault and security management.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

MATH 162M or familiarity with computer security area

This course is to help students gain a thorough understanding of vulnerabilities and attacks in systems and networks and learn cyber defense best practices. It covers fundamental security design principles and defense strategies and security tools used to mitigate various cyber attacks. The topics may include identification of Recon Ops, intrusion detection, identification of C2 Ops, data exfiltration detection, identifying malicious codes, network security techniques, cryptography, malicious activity detection, system security architectures, defense in depth, distributed/cloud and virtualization.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

The course introduces classical and advanced models and techniques in machine learning and deep learning. It applies these techniques in the cybersecurity domain including anomaly detection, network security, and malware detection and classification. Advanced applications such as self- driving cars and IoT systems are also discussed. In addition, cyber-attacks on machine learning techniques and AI systems and the possible consequences are also discussed. cybersecurity

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 462/CS 562 or CS 465/CS 565 or experience in

This course provides an overview of the fundamentals of computer architecture. Major components include basic computer logic, integrated circuits, instruction sets, cache-RAM interaction, virtual memory, the fetch/ execute cycle, basics of microprogramming, pipelined implementation, parallel processors including SISD, MIMD, SIMD, and SPMD. The course also introduces hardware multithreading, multicores, multiprocessors, GPUs, multiprocessor network topologies, and cluster networking. 473 CS - Computer Science

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Instructor permission required

Operating system structures. Multiprogramming and multiprocessing. Process management. Memory and other resource management. Storage management, I/O systems, distributed systems. Protection and security. The concepts will be illustrated through example systems such as Unix and Windows. CS 170; a grade of C or better in ENGN 122 or CS 150 or CS 260

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

ECE 346 or ECE 443 or a grade of C or better in CS 361 and

Efficient implementation methods. Time management. Planning and design of simulation experiments. Statistical issues in simulation. Generation of random numbers and stochastic variates. Programming with graphically- and text-based simulation languages. Verification and validation of simulation models. Distributed simulation. Special topics such as HLA will be discussed.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. This course is to help students fully understand and utilize the internal workings and capabilities provided by modern computing, networking and programming environments. Topics include: Shell Script Programming, X Windows (Xlib and Motif), UNIX internals (I/O, Processes, Threads, IPC and Signals), Network Programming (UDP/ TCP Sockets and Multicasting) and Java Systems Programming (SWING, Multithreading and Networking).

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Introduction to concepts, principles, challenges, and research in major areas of AI. Areas of discussion include: natural language and vision processing, machine learning, machine logic and reasoning, robotics, expert and mundane systems.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course offers a comprehensive, project-driven approach to integrating AI in healthcare, focusing on building prototype models for prevention, diagnosis, and treatment while stressing the need of adopting ethical, responsible methods to reduce risks and improve patient outcomes.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. The motivation for and successes of parallel computing. A taxonomy of commercially available parallel computers. Strategies for parallel decompositions. Parallel performance metrics. Parallel algorithms and their relation to corresponding serial algorithms. Numerous examples from scientific computing, mainly in linear algebra and differential equations. Implementations using public-domain network libraries on workstation clusters and computers.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Theoretical and practical aspects of compiler design and implementation. Topics will include lexical analysis, parsing, translation, code generation, optimization, and error handling.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Students perform mentored research in a group environment to develop computational approaches in addressing computer science challenges. The project needs approval by the Computer Science Honors Program director, and registration requires approval of the research mentor and the Graduate Program Director. A GPA of 3.00 or better is required, or approval by the director of the Computer Science Honors Program.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Students continue mentored research using the project defined in CS 591. Students will present the work and findings to the public. The project needs approval by the Computer Science Honors Program director, and registration requires approval of the research mentor and the Graduate Program Director. A GPA of 3.00 or better is required, or approval by the director of the Computer Science Honors Program.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

A grade of B or better in CS 591

Special topics.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Independent study under the direction of an instructor.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

This course covers the following topics: (i) Basic introduction to algorithms, their design and analysis (ii) Asymptotic notation (iii) Recurrence Relations and their solutions (iv) Sorting and Order Statistics: various algorithms for sorting and their analysis, lower bounds for sorting, computing medians, modes and various order statistics (v) Paradigms for algorithm design and analysis: Dynamic Programming, Greedy Method, Amortized Analysis, and (vi) Graphs and Elementary Graph Algorithms: Breadth-first and Depth- first Search, Topological Sort, Minimum Spanning Trees and Shortest Paths Algorithms.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 361 or equivalent and CS 381 or equivalent

This course will explore data science as a burgeoning field. Students will learn fundamental principles and techniques that data scientists employ to mine data. They will investigate real life examples where data is used to guide assessments and draw conclusions. This course will introduce software and computing resources available to a data scientist to process, visualize, and model different types of data including big data. Cross-listed with DASC 620.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Alias

DASC 620

6 CS 625 Data Visualization 3

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real- world datasets to reinforce the concepts covered in the course.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This is a first course in parallel architecture, with an emphasis on the description and evaluation of commercially available machines. Topics to be covered include: parallelization and performance metrics, scalability and the "laws" of Amdahl and Gustavson, computational similarity, models of computation, parallelization paradigms, network characteristics and topology, communication calculus and templates, pipelining and parallelism, processor types, memory hierarchy, cache coherence protocols, latency- hiding mechanisms, routing algorithms, and languages and libraries to support parallel architecture.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 665

Laboratory work required. The course covers digital image processing techniques including representation, sampling and quantization, imaging geometry, image transforms, image enhancement, image filtering, color image processing, image segmentation, and morphological image processing. Applications include image restoration, image compression, pattern recognition, and image fusion.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

This course focuses on advanced database systems, with an emphasis on NoSQL databases to handle the challenges of scalability, flexibility, and high availability in real-world applications. Unlike typical database courses, it delves into the distinct data models and architectures of NoSQL systems, such as document, key-value, column-family, and graph databases. Students will receive hands-on experience developing safe, distributed, and scalable NoSQL-based solutions for modern data-intensive applications.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Laboratory work required. Analysis, design and implementation of databases and database applications using modern software engineering methods. Database CASE tools. Analysis using process, function, and dataflow analysis in conjunction with entity relationship modeling. Database diagrams and database design. Application suite design and high level design of applications. Refining implementations.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 550

Laboratory work required. The mathematical tools needed for the geometrical aspects of 3D computer graphics. Fundamentals: homogeneous coordinates, transformations and perspective. Theory of parametric and implicit curve and surface models: polar forms, Bezier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Representations of solids and conversions among them. Beometric algorithms for graphics problems, with applications to ray tracing, hidden surface elimination, etc.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 560

CS Electives 15 Total Credit Hours 30 Additional Notes: • CS Electives may include up to 6 credits of CS 898 Doctoral Research. • At least three-fifths of the minimum required hours for a doctoral degree must be completed at the 800 level. This means that students required to take 78 credit hours must have at least 47 credit hours at the 800 level. All students will complete at least 35 credit hours at the 800 level by satisfying the CS 800, CS 899, and breadth course requirements, so 12 additional credit hours of 800 level courses are required.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Requirements will be established by the department and will vary with the amount of credit desired. Allows students an opportunity to gain a short duration career-related experience.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Total Credit Hours 16

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Old Dominion University Graduate Catalog 2025-2026 474

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Independent study under the direction of an instructor.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

Total Credit Hours 31 The candidate is required to prepare a written report on the project and to present it orally.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Total Credit Hours 31 Students in the thesis option are required to take CS 600 and CS 650 as part of the core coursework requirement. The candidate is required to write a thesis and make an oral presentation of the results.

Subject

CS

Credits (min)

6

Credits (max)

6

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Stochastic processes are ways of quantifying the dynamic relationship of sequences of random events. This course will expose the participants to standard concepts and methods of stochastic modeling, as well as the rich diversity of applications. Topics include, but not limited to, Markov chains in discrete and continuous time, Poisson processes, renewal theory and branching processes.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course serves to illustrate important principles in Monte Carlo simulation methods and to demonstrate their power in applications. The course covers Metropolis-Hastings algorithm, Gibbs sampler, Markov Chain Monte Carlo, acceptance-rejection method, Monte Carlo integration, quasi- Monte Carlo, random walk, and random number generation.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

or CS 733 Natural Language Processing or CS 728 Deep Learning Fundamentals and Applications

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces the fundamental knowledge in bioinformatics and the current advances in selected directions. The topics include: fundamental concepts and experimental techniques in molecular biology, computational methods in genomic sequence comparison and analysis, and computational methods in molecular structural modeling.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces parallel and distributed programming principles and has emphasis on hands-on programming and deploying high-performance computing applications with big data for different science and engineering disciplines. Topics includes programming on emerging technologies such as NVIDIA GPU, Hadoop Framework, and Apache Spark for large scale data analytics and mining applications.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization technique with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 625

This course links the fundamental concepts and algorithms of graphs with the actual biological problems. Various biological problems will be selected to discuss the formulation of the graph, the graph algorithms, and the performance analysis.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

The goal of this course is to understand fundamental concepts and to survey current advances in computational structural bioinformatics. In the scope of computational structural bioinformatics, computational methods are developed to address 3-dimentaional structure-related biological problems that often involve protein and RNA. The topics include basics of protein, DNA and RNA structures, principle of protein structure prediction, deep learning in protein structure problems and cryo-electron microscopy data and challenges. 475 CS - Computer Science

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces students to principles and research methods in human-computer interaction (HCI), an interdisciplinary area studying the interaction between humans and interactive computing systems. Students will learn to model computer users and interfaces, significant cognitive and social phenomena surrounding the human use of computers, apply empirical techniques for task analysis and interface design, and evaluate designs qualitatively and quantitatively.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience required

Natural language processing (NLP) techniques are the crux of many leading modern technologies. Advances in NLP are also critical in the pursuit of Artificial Intelligence. This course will discuss core problems in NLP and the state-of-the-art tools and techniques as well as advanced NLP research topics. The topics will include language models, part-of-speech tagging, syntactic parsing, word embedding, statistical machine translation, text summarization, question answering, and dialog interaction. At the end of the course, students will be familiar with many language-processing tasks and applications.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 580

Laboratory work required. Theory and engineering of information retrieval in the context of developing web-based search engines. Topics include issues related to crawling, ranking, query processing, retrieval models, evaluation, clustering, machine learning, and other aspects related to building web search engines. Students will perform a mix of hands-on development and coding, as well as theoretical exploration of the research literature.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Explores the veracity of information on the web and social media. Digital information is easy to manipulate, copy, and delete, but web archives offer a trusted method for timestamping the appearance of web pages and their contents. Students will investigate how web archives can be used to establish the priority of information, as well as how they can be hacked or used to obfuscate the provenance of falsified content. utilities

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Graduate standing and familiarity with command line

This course covers the following topics: Image processing and filters; Edges and features; Interest points and features; Bag of words representation; Convolutional neural networks; Object detection; Image formation; 3D reconstruction; Motion analysis; and Light and shading. courses with the permission of the instructor

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 422 and CS 480, or CS 522 and CS 580, or equivalent

This course introduces the basic concepts of computational imaging. The topics include principles of imaging systems, role of computational methods in enhancing imaging systems, computational imaging inverse problems, and data-driven machine learning approaches to solve inverse problems in computational imaging. experience

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of linear algebra and prior programming

Subject

CS

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Networks (3 Credit Hours) The course will introduce some of the commonly used techniques in the performance evaluation of computing systems. Students will be exposed to a variety of analytical and simulation tools used in this field. The applicabilit of the techniques will be illustrated through case studies.

Subject

CS

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

The course is project-driven and is designed to help transition from theory to actual implementation, particularly in utility-scale quantum computing. The course covers (i) the fundamental concepts of quantum information and computation, (ii) mapping computational problems onto quantum circuits, (iii) understanding noise in near-term quantum computers, and (iv) executing quantum circuits on real quantum hardware.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of Python, Linear Algebra, and Discrete

Digital Libraries (DLs) are an increasingly popular research area that encompass more than traditional information retrieval or database methods and techniques. The course will cover a brief history of DL development, with emphasis on World Wide Web implementations. Case studies will be performed on various DLs. The class will focus heavily on project work. At the end of the course, students will be prepared to develop, evaluate, or apply digital library technologies in their work environment. Topics include: Repositories; Distributed Searching; Metadata Harvesting; Preservation, Reference Linking and Citation Analysis.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Theory and practice in analysis and mitigation of malware in networked machines. Theoretical topics include methods of attack anatomy, identification, reverse and anti-reverse engineering. Practice entails learning tools and techniques used by malware attackers, defenders and analysts in lab-based projects conducted in a secure 'sandbox' mode.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course is based on the Intel processor architecture employed in Windows, Linux and MacOS operating systems. Students will learn how memory is assigned to processes and how it is addressed, how memory data structures can be exploited by malware, and what is available for forensic analysis of memory. The course involves several hands-on lab work on recognizing process data structures in memory, memory acquisition, and use of a set of tools to catch the malware while preserving evidence from live memory analysis. Course requires a set of assigned reading and lab work.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 270, CS 471, or instructor's permission

Technologies, and Economics (3 Credit Hours) This course covers different aspects of cryptocurrencies, including P2P networks, distributed consensus, Bitcoin and Ethereum, blockchain technologies, cryptographic techniques (secure hashing, encryption, decryption, digital signatures), privacy and anonymity, mining and mining puzzles, wallets, smart contracts, case studies, cryptocurrency ecosystem, legal aspects, implications and impact on economy and finance, and future of cryptocurrencies.

Subject

CS

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 471, CS 455/CS 555 or equivalent experience

This course covers various topics in Internet of Things (IoT) security, including web security, network security, mobile app security and secure cryptocurrency. It provides an in-depth study of various attack techniques and methods to defend against them. The course adopts the 'learning by y doing' principle. Students are supposed to learn the attacks by performing them in a networked virtual machine environment. They will also play with a number of security tools to understand how they work and what security guarantee they provide. Laboratory work required. operating systems; no prior knowledge of computer security is necessary

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

basic knowledge of programming, computer networks and

This course is a research-oriented, graduate-level course, centering around basic protocols and technique, as well as advanced, state-of-the-art topics to secure computer and Internet services. Topics include: System and Software Security, Cryptography and PKI, Internet Infrastructure and Network Security, Web and Browser Security, Cloud Security, and Online Privacy.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 455 or CS 555

Introduction to data mining; Algorithms including naive Bayes, Decision Trees and Rules, Association Rules, Linear classification, and Clustering; Cross validation, Lift charts, ROC Curves; SVM, Bayesian networks, K-means clustering; Data transformation; PCA; Ensemble Learning; Application of data mining to security and privacy including authentication, authorization, and intrusion detection; Privacy-preserving data mining.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 471 and CS 455 or CS 555

Cloud computing requires a great deal of architectural support. This course investigates various types of architectural support that make cloud computing almost infinitely scalable while maintaining efficiency. The course will look at various types of support provided by Google, Amazon, Facebook, Yahoo! and others.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Understanding the design, implementation and performance of network protocols using TCP/IP protocol suite as a case study. The students will have hands-on experience on low-level tools and will access and study the source code of these protocols and writing networking software applications. Topics include: socket interface, IPv4 and IPv6, routing, UDP, multicasting and IGMP, TCP specification, implementation and performance.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 455/CS 555 or equivalent

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences. Old Dominion University Graduate Catalog 2025-2026 476

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

Seminar.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

Topics in computer science.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Topics in computer science.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

CS courses that meet the breadth course requirements (see below) 12 CS Electives 9

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Stochastic processes are ways of quantifying the dynamic relationship of sequences of random events. This course will expose the participants to standard concepts and methods of stochastic modeling, as well as the rich diversity of applications. Topics include, but not limited to, Markov chains in discrete and continuous time, Poisson processes, renewal theory and branching processes.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course serves to illustrate important principles in Monte Carlo simulation methods and to demonstrate their power in applications. The course covers Metropolis-Hastings algorithm, Gibbs sampler, Markov Chain Monte Carlo, acceptance-rejection method, Monte Carlo integration, quasi- Monte Carlo, random walk, and random number generation.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course presents both the foundational and the practical aspects of modeling, analyzing, and mining of computerized data sets, including classification, regression, clustering, semi-supervised learning, structured sparsity learning, etc. The course assignments are designed to contain both theoretical and programming components in order to train students to gain hands-on-experience.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Select two of the following: 6

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces parallel and distributed programming principles and has emphasis on hands-on programming and deploying high-performance computing applications with big data for different science and engineering disciplines. Topics include programming on emerging technologies such as NVIDIA GPU, Hadoop Framework, and Apache Spark for large scale data analytics and mining applications. 477 CS - Computer Science

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course covers the theory and application of information visualization and of visual analytics, the science of combining interactive visual interfaces and information visualization technique with automatic algorithms to support analytical reasoning through human-computer interaction. Research on visual perception, cognition, interactive visual interfaces, and visual analytics will be covered. Practical techniques for the display of complex multivariate data will be addressed. Course projects will require the development of interactive web-based interfaces to analyze and visualize real-world datasets.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 625

This course links the fundamental concepts and algorithms of graphs with the actual biological problems. Various biological problems will be selected to discuss the formulation of the graph, the graph algorithms, and the performance analysis.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

The goal of this course is to understand fundamental concepts and to survey current advances in computational structural bioinformatics. In the scope of computational structural bioinformatics, computational methods are developed to address 3-dimentaional structure-related biological problems that often involve protein and RNA. The topics include basics of protein, DNA and RNA structures, principle of protein structure prediction, deep learning in protein structure problems and cryo-electron microscopy data and challenges.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course introduces students to principles and research methods in human-computer interaction (HCI), an interdisciplinary area studying the interaction between humans and interactive computing systems. Students will learn to model computer users and interfaces, significant cognitive and social phenomena surrounding the human use of computers, apply empirical techniques for task analysis and interface design, and evaluate designs qualitatively and quantitatively.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience required

Natural language processing (NLP) techniques are the crux of many leading modern technologies. Advances in NLP are also critical in the pursuit of Artificial Intelligence. This course will discuss core problems in NLP and the state-of-the-art tools and techniques as well as advanced NLP research topics. The topics will include language models, part-of-speech tagging, syntactic parsing, word embedding, statistical machine translation, text summarization, question answering, and dialog interaction. At the end of the course, students will be familiar with many language-processing tasks and applications.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 580

Laboratory work required. Theory and engineering of information retrieval in the context of developing web-based search engines. Topics include issues related to crawling, ranking, query processing, retrieval models, evaluation, clustering, machine learning, and other aspects related to building web search engines. Students will perform a mix of hands-on development and coding, as well as theoretical exploration of the research literature.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Explores the veracity of information on the web and social media. Digital information is easy to manipulate, copy, and delete, but web archives offer a trusted method for timestamping the appearance of web pages and their contents. Students will investigate how web archives can be used to establish the priority of information, as well as how they can be hacked or used to obfuscate the provenance of falsified content. utilities

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Graduate standing and familiarity with command line

This course covers the following topics: Image processing and filters; Edges and features; Interest points and features; Bag of words representation; Convolutional neural networks; Object detection; Image formation; 3D reconstruction; Motion analysis; and Light and shading. courses with the permission of the instructor

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 422 and CS 480, or CS 522 and CS 580, or equivalent

This course introduces the basic concepts of computational imaging. The topics include principles of imaging systems, role of computational methods in enhancing imaging systems, computational imaging inverse problems, and data-driven machine learning approaches to solve inverse problems in computational imaging. experience

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of linear algebra and prior programming

Subject

CS

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Networks (3 Credit Hours) The course will introduce some of the commonly used techniques in the performance evaluation of computing systems. Students will be exposed to a variety of analytical and simulation tools used in this field. The applicabilit of the techniques will be illustrated through case studies.

Subject

CS

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

The course is project-driven and is designed to help transition from theory to actual implementation, particularly in utility-scale quantum computing. The course covers (i) the fundamental concepts of quantum information and computation, (ii) mapping computational problems onto quantum circuits, (iii) understanding noise in near-term quantum computers, and (iv) executing quantum circuits on real quantum hardware.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Knowledge of Python, Linear Algebra, and Discrete

Digital Libraries (DLs) are an increasingly popular research area that encompass more than traditional information retrieval or database methods and techniques. The course will cover a brief history of DL development, with emphasis on World Wide Web implementations. Case studies will be performed on various DLs. The class will focus heavily on project work. At the end of the course, students will be prepared to develop, evaluate, or apply digital library technologies in their work environment. Topics include: Repositories; Distributed Searching; Metadata Harvesting; Preservation, Reference Linking and Citation Analysis.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Theory and practice in analysis and mitigation of malware in networked machines. Theoretical topics include methods of attack anatomy, identification, reverse and anti-reverse engineering. Practice entails learning tools and techniques used by malware attackers, defenders and analysts in lab-based projects conducted in a secure 'sandbox' mode.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course is based on the Intel processor architecture employed in Windows, Linux and MacOS operating systems. Students will learn how memory is assigned to processes and how it is addressed, how memory data structures can be exploited by malware, and what is available for forensic analysis of memory. The course involves several hands-on lab work on recognizing process data structures in memory, memory acquisition, and use of a set of tools to catch the malware while preserving evidence from live memory analysis. Course requires a set of assigned reading and lab work.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 270, CS 471, or instructor's permission

Technologies, and Economics (3 Credit Hours) This course covers different aspects of cryptocurrencies, including P2P networks, distributed consensus, Bitcoin and Ethereum, blockchain technologies, cryptographic techniques (secure hashing, encryption, decryption, digital signatures), privacy and anonymity, mining and mining puzzles, wallets, smart contracts, case studies, cryptocurrency ecosystem, legal aspects, implications and impact on economy and finance, and future of cryptocurrencies.

Subject

CS

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 471, CS 455/CS 555 or equivalent experience

This course covers various topics in Internet of Things (IoT) security, including web security, network security, mobile app security and secure cryptocurrency. It provides an in-depth study of various attack techniques y and methods to defend against them. The course adopts the 'learning by doing' principle. Students are supposed to learn the attacks by performing them in a networked virtual machine environment. They will also play with a number of security tools to understand how they work and what security guarantee they provide. Laboratory work required. operating systems; no prior knowledge of computer security is necessary

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

basic knowledge of programming, computer networks and

This course is a research-oriented, graduate-level course, centering around basic protocols and technique, as well as advanced, state-of-the-art topics to secure computer and Internet services. Topics include: System and Software Security, Cryptography and PKI, Internet Infrastructure and Network Security, Web and Browser Security, Cloud Security, and Online Privacy.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 455 or CS 555

Introduction to data mining; Algorithms including naive Bayes, Decision Trees and Rules, Association Rules, Linear classification, and Clustering; Cross validation, Lift charts, ROC Curves; SVM, Bayesian networks, K-means clustering; Data transformation; PCA; Ensemble Learning; Application of data mining to security and privacy including authentication, authorization, and intrusion detection; Privacy-preserving data mining.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 471 and CS 455 or CS 555

Cloud computing requires a great deal of architectural support. This course investigates various types of architectural support that make cloud computing almost infinitely scalable while maintaining efficiency. The course will look at various types of support provided by Google, Amazon, Facebook, Yahoo! and others. Old Dominion University Graduate Catalog 2025-2026 478

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Understanding the design, implementation and performance of network protocols using TCP/IP protocol suite as a case study. The students will have hands-on experience on low-level tools and will access and study the source code of these protocols and writing networking software applications. Topics include: socket interface, IPv4 and IPv6, routing, UDP, multicasting and IGMP, TCP specification, implementation and performance.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

CS 455/CS 555

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Subject

CS

Credits (min)

3

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Prior programming experience

Seminar.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

Topics in computer science.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Topics in computer science.

Subject

CS

Credits (min)

1

Credits (max)

3

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

permission of the instructor

Independent study at the doctoral level under the direction of an instructor.

Subject

CS

Credits (min)

1

Credits (max)

9

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

Prerequisite

Permission of the instructor

Research for the doctoral dissertation. Departmental permission required.

Subject

CS

Credits (min)

1

Credits (max)

9

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course is a pass/fail course for master's students in their final semester. It may be taken to fulfill the registration requirement necessary for graduation. All master's students are required to be registered for at least one graduate credit hour in the semester of their graduation.

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu

This course is a pass/fail course doctoral students may take to maintain active status after successfully passing the candidacy examination. All doctoral students are required to be registered for at least one graduate credit hour every semester until their graduation. CSD - Communication Sciences and

Subject

CS

Credits (min)

1

Credits (max)

1

Type

course

Edition

2025-2026-graduate

Source

catalog.odu.edu