Computer Science (CS)

CS 1308. Computer Literacy and the Internet.

This course provides an introduction to fundamental computing concepts for students with little or no prior background in computer science. Topics include introductory concepts in computing systems and hardware, data representation, networking and internet fundamentals, cybersecurity, artificial intelligence and data science. Students develop practical skills using common productivity software, including word processing, spreadsheets, and presentations, and examine basic algorithmic problem solving techniques. The course emphasizes understanding how computing technologies function and how they are used across academic, professional, and everyday contexts. Designed for non majors, the course offers foundational knowledge for informed engagement with digital systems.

CS 1309. AI for Everyone.

This course provides an introduction to the principles and techniques used in artificial intelligence (AI). It covers both foundational and advanced principles underlying AI and explains the differences between AI, machine learning, and deep learning. It explores critical topics such as neural networks, natural language processing, reinforcement learning, and robotics. Students will gain experience with generative AI tools and prompt engineering. Ethical questions and potential sources of bias in AI systems are studied as topics of analysis to support informed evaluation. This course will not satisfy CS major or minor requirements.

CS 1319. Fundamentals of Computer Science.

This course introduces fundamental concepts of computer science through a breadth‑first examination of major computing layers, including information, hardware, programming, operating systems, applications, and communication systems. The course examines historical developments, societal influences, and technical constraints that have shaped modern computing. Emphasis is placed on conceptual understanding supported by practical examples aligned with widely recognized computing curricula. Students complete basic computing exercises to develop familiarity with how computer systems function and interact. This course is designed for non‑majors and does not apply toward computer science major or minor requirements.

CS 1342. Programming for Scientists and Engineers.

This course examines computational problem solving and program development in the context of engineering and the physical sciences. Students analyze scientific and engineering problems and construct algorithmic solutions using structured programming techniques in a high-level language. Topics include fundamental concepts of computer organization, data representation, expressions, control structures, functions, arrays and collections, and modular program design. Emphasis is placed on systematic approaches to testing and debugging, effective program organization, and the relationship between algorithm design, data handling, and the correctness and reliability of computational solutions. The course also considers computational tools and practices commonly used in scientific computing and data analysis.

CS 1428. Foundations of Computer Science I.

This course examines foundational concepts in computer science through problem solving and algorithm development. It focuses on how computational problems are analyzed, designed, and implemented using structured programming techniques in C++. Students analyze problems and construct solutions using primitive and complex data types, expressions, control structures, functions, arrays, and related constructs. Clear program organization, consistent and readable coding style, and systematic approaches to testing and debugging are central themes. Particular attention is given to the relationship between algorithm design, program structure, and the correctness and reliability of software. Prerequisite: [MATH 1315 or MATH 1317 or MATH 1319 or MATH 1329 or MATH 2417 or MATH 2471 with a grade of "C" or better] or [ACT Mathematics score of 24 or better] or [New ACT Mathematics score of 25 or better] or [SAT Mathematics score of 520 or better] or [SAT Math Section score of 550 or better] or [Accuplacer College Mathematics score of 86 or better] or [Compass College Algebra score of 46 or better] or [Next-Generation Advanced Algebra and Functions Test of 263 or better].

CS 2308. Foundations of Computer Science II.

This course examines intermediate concepts in computer science through the design, implementation, and analysis of larger programs in C++. Topics include abstract data types (ADTs) such as lists, stacks, and queues, and their implementation using arrays and linked lists. Students learn how pointers, dynamic memory allocation, and linked structures support flexible data representation and manipulation. The course emphasizes object-oriented programming, including classes, objects, encapsulation, and the separation of interface and implementation. Students also study fundamental searching and sorting algorithms and analyze their performance using time complexity. Program development, compilation, and testing are performed in a Linux command-line environment. Prerequisite: CS 1428 with a grade of "C" or better.

CS 2315. Computer Ethics.

This course introduces students to ethical, legal, and professional issues that arise in a world shaped by pervasive computing and rapidly evolving AI. Grounded in classical and contemporary ethical frameworks, the course develops students’ ability to analyze real-world cases involving privacy, security, intellectual property, automation, accessibility, globalization, and the social impact of computing on race, class, and gender. Through intensive writing, collaborative presentations, and a substantial term paper, students practice clear communication for diverse audiences, apply professional codes of ethics, and critically evaluate sources. Emphasis is placed on civic and professional responsibility, helping future computing professionals anticipate downstream consequences of technology and justify responsible courses of action. Prerequisites: CS 1428 and [COMM 1310 or COMM 2338] and [ENG 1310 or ENG 1320 or ENG 1321 or ENG 3303] and [PHIL 1305 or PHIL 1320] with grades of "C" or better.

CS 2318. Assembly Language.

This course provides a foundational exploration of digital data representation, computer organization, and assembly language programming. Students will examine how machine-level instructions are structured and executed, how processors manage and manipulate data, and how high-level programming constructs are translated into low-level executable code. Through a combination of written assignments and hands-on programming exercises, students gain practical experience working directly with hardware abstractions. By the end of the course, students will be equipped to explain core architectural components, analyze and describe instruction set formats, and design and implement fundamental algorithms using assembly language. Prerequisite: CS 2308 and MATH 2358 with grades of "C" or better.

CS 2325. Computer Organization.

This course provides an introduction to computer systems from a programmer's perspective. Students become familiar with computer organization by learning assembly language and studying computer architecture. Topics include data representation, assembly language fundamentals and programming, the compilation process, processor components, logic circuits, and memory hierarchy design. The fundamental understanding of computer organization provided by this course will enable students to write more efficient programs and prepare them for advanced computer architecture courses. Prerequisite: CS 2308 and MATH 2358 both with grades of "C" or better.

CS 3190. Cooperative Education.

This course provides students participating in approved computer science internships the opportunity to integrate academic knowledge with professional practice in an occupational setting. Students apply concepts from prior coursework to technical tasks encountered in the workplace and analyze how academic preparation supports professional responsibilities. The course emphasizes reflection on problem solving processes, technical communication, and skill development through supervised work experience. Students complete structured reports or presentations that examine the relationship between classroom learning and workplace activities. A maximum of three credit hours of cooperative education may be applied toward the computer science major as elective credit. Prerequisite: Minimum 2.25 Overall GPA and instructor approval.

CS 3290. Advanced Cooperative Education.

This course provides students participating in approved computer science internships the opportunity to integrate academic knowledge with professional practice in an occupational setting. Students apply concepts from prior coursework to technical tasks encountered in the workplace and analyze how academic preparation supports professional responsibilities. The course emphasizes reflection on problem solving processes, technical communication, and skill development through supervised work experience. Students complete structured reports or presentations that examine the relationship between classroom learning and workplace activities. A maximum of three credit hours of cooperative education may be applied toward the computer science major as elective credit. Prerequisite: A minimum 2.25 Overall GPA and instructor approval.

CS 3320. Internet Software Development.

This course presents internet web development using a full-stack methodology covering both static and dynamic web pages with client-side and server-side components, and includes the networking concepts necessary for communication between layers. It begins with client-side static web pages and progresses through styling, consuming web APIs from the client side, implementing web APIs on the server side, and persisting data in an external data store. It culminates with a final capstone project to demonstrate mastery and interworking of all layers. Prerequisite: CS 2308 with a grade of "C" or better.

CS 3339. Computer Architecture.

This course equips students with a foundational understanding of how modern computers are designed and built from the ground up. Students will explore how hardware components, from arithmetic logic units to memory systems, work together to execute instructions efficiently. Through hands-on engagement with topics like instruction set architectures, pipelining, multiprocessing, and virtual memory, students develop the analytical skills needed to evaluate performance trade-offs and write lower-level programs. By the end of the course, students will understand not just what a computer does, but precisely how and why it does it. Prerequisite: CS 2308 and [CS 2318 or CS 2325 or EE 3320] with grades of "C" or better.

CS 3354. Object-Oriented Design and Programming.

This course covers object-oriented design principles and programming for students with prior programming experience. Students will explore inheritance and polymorphism, object-oriented design processes, UML diagrams, design patterns, exception handling, multithreading, testing, performance profiling, and the use of AI in extending and refactoring software systems. Students will design and implement Java programs using design patterns and apply event-driven programming principles by developing graphical user interfaces with a Java graphics library. Thread concurrency is also explored through Java programs that manage shared data via read and write operations. Prerequisite: CS 3358 with a grade of "C" or better.

CS 3358. Data Structures and Algorithms.

This course examines classic data structures and algorithms, including abstract data types such as sets, lists, stacks and queues, as well as trees, heaps, hash tables, and graphs, with emphasis on algorithm design and quantitative analysis of time and space complexity. Students explore generic programming, sorting and searching algorithms and recursion, gaining exposure to a wider range of problem-solving strategies. By the end of the course, students are expected to demonstrate proficiency in selecting appropriate data structures, applying algorithms to problem solving, and analyzing algorithmic performance using Big-O complexity analysis and other established complexity measures. Prerequisite: CS 2308 and MATH 2358 with grades of "C" or better.

CS 3360. Computing Systems Fundamentals.

This course covers fundamental concepts underlying the design and implementation of computing systems. Topics include performance evaluation, queueing systems and scheduling, communication and networks, parallel programming and synchronization. Students will explore key concepts, examine critical issues, and study classic solutions across these topics. After completing the course, students should be able to describe a variety of problems that reoccur in computing systems, analyze the causes and impacts of those problems, and apply appropriate tools and algorithms to solve them. Prerequisite: [CS 2318 or CS 2325 or EE 3320] and CS 3358 with grades of "C" or better.

CS 3378. Theory of Automata.

This course introduces the fundamental concepts and principles of automata theory, formal languages and computability. Course topics include finite state machines, pushdown automata, Turing machines, regular grammar, regular expression, context free grammar, Church-Turing thesis, Halting problem, and reduction proofs. The course is theoretical and mathematical in nature, however, interactive simulation tools such as JFLAP will be used to help students visualize, design, and simulate abstract machines. Mini programming projects will be assigned to help students connect theory with practice. Prerequisite: CS 3358 with a grade of "C" or better.

CS 3398. Software Engineering.

This course introduces the study of software design, architecture, implementation, testing and validation techniques through team projects. Through team projects, students will learn to self-organize, plan deliverables, assign work, report status and present demonstrations that allow all stakeholders to assess the quality, features, architecture and schedule of the projects. Students will complete projects that approximate industry experience using skills such as Agile sprints, stand-ups, end-of-sprint presentations and reviews, use of AI, and version control, as well as architecture design and documentation. Prerequisite: CS 3354 and CS 3358 and [CS 2315 or EE 2300] with grades of "C" or better.

CS 4100. Computer Science Internship.

This course provides CS undergraduate students on-the-job training supervised by computer professionals in industry internship programs. During the internship, students apply what they have learned in the classroom toward the internship work. They are required to submit a mid-term and a final report of their internship work, describing what they have learned as a result of the internship and what curriculum additions or improvements they would suggest as a result of the internship experience. Students need the approval of the department in advance to take this internship course. Prerequisite: Minimum 2.25 Overall GPA and instructor approval.

CS 4298. Undergraduate Research I.

This course allows interested undergraduate students to pursue an individual research project under the supervision of a computer science faculty member. The course content depends on the chosen research project but typically includes an in-depth study of the selected topic, a review of related work, as well as the implementation of an idea. It is the first course of a two-semester course sequence and cannot be given degree credit until the satisfactory completion of CS 4299. Prerequisites: Minimum 3.00 Major GPA and instructor approval.

CS 4299. Undergraduate Research II.

This course allows interested undergraduate students to pursue an individual research project under the supervision of a computer science faculty member. The course content depends on the chosen research project but typically includes the evaluation of the idea implemented in CS 4298 as well as the presentation and a write-up of the project and its results. It is the second course of a two-semester course sequence and can only be taken after the satisfactory completion of CS 4298. Prerequisites: Instructor Approval.

CS 4310. Computer Networks.

This course introduces the fundamental concepts underlying the design and operation of computer networks, with emphasis on the Internet’s TCP/IP architecture. Students explore layered network models, packet switching, IP addressing, routing, transport protocols, congestion control, wireless networking, and foundational security concepts. The course emphasizes protocol behavior, performance analysis, and hands‑on investigation using tools such as Wireshark. By the end of the course, students will have a strong foundation for designing, evaluating, and troubleshooting networked systems, preparing them for advanced study in networking and cybersecurity. Prerequisite: CS 3360 with a grade of "C" or better.

CS 4315. Introduction to Data Mining and Information Retrieval.

This course covers fundamental principles and techniques in two closely related fields of data mining and information retrieval. Data mining topics include classification, cluster analysis, and frequent pattern mining. Information retrieval topics include Boolean retrieval, vector space model, and web search. Through programming assignments and projects, students will enhance their understanding of theoretical concepts and gain hands-on experience on practical techniques. They will also explore popular tool packages such as Weka, Orange, Apache Lucene, and Elasticsearch. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4318. Compiler Construction.

This course examines the design and implementation of compilers, focusing on the translation of high-level programs into executable code. Topics include lexical analysis, parsing, semantic analysis, intermediate representations, optimization, and code generation, emphasizing correctness and performance across architectures. Students construct components of a compiler and integrate them into a working system, applying formal models, data structures, and algorithms to solve translation problems. By the end of the course, students produce executable code from source programs, analyze design trade-offs in compilation strategies, and evaluate the impact of optimizations on program behavior and performance. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4328. Operating Systems.

This course examines the principles and design of modern operating systems. Topics include process and thread management, CPU scheduling, synchronization, interprocess communication, deadlocks, memory management, virtual memory, file systems, I/O, virtualization, and cloud computing. Students explore system calls, kernel structure, and security mechanisms, gaining insight into how operating systems manage and allocate hardware resources efficiently. Emphasis is placed on the interaction between hardware and software and on performance trade-offs inherent in real-world system design. Programming assignments provide hands-on experience with system-level concepts using a Unix/Linux environment, reinforcing theoretical knowledge through practical implementation. Prerequisite: [CS 2325 or CS 3339] and CS 3360 with grades of "C" or better.

CS 4332. Introduction to Database Systems.

This course introduces students to the fundamental concepts and practices of database management systems. Topics include data modeling methodologies, relational database design, normalization techniques, and Entity-Relationship (ER) diagramming. Students will develop proficiency in Structured Query Language (SQL) and gain hands-on experience with industry-standard Database Management System (DBMS) platforms. Additional topics include file structures, query optimization, and database performance tuning. By the end of the course, students will be equipped to design, implement, and maintain relational databases for real-world applications. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4337. Introduction to Computer Vision.

This course provides a comprehensive foundation in image processing and computer vision, spanning both classical analytical techniques and modern data-driven deep learning paradigms. Students explore core topics ranging from edge detection, morphological operations, and dimensionality reduction to convolutional neural networks, object detection, and generative vision. The curriculum employs a rigorous, hands-on programmatic methodology that requires students to build and train algorithms capable of processing complex visual data. Ultimately, students will develop the capacity to design, implement, and critically evaluate computer vision systems for real-world applications while navigating the ethical implications of artificial intelligence. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4346. Introduction to Artificial Intelligence.

This course introduces the fundamental principles of Artificial Intelligence (AI) and Generative AI, highlighting the relationship between traditional AI techniques and modern generative methods. Students study problem-solving, system design, and ethical considerations in computing while building a foundation in core AI methodologies, including search strategies, knowledge representation, basic machine learning, and generative models. Students also explore applications such as question-answering systems, game playing, and planning, and learn how to design and implement intelligent systems. By the end of the course, students will be able to apply AI and Generative AI techniques to develop intelligent systems for real-world applications. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4347. Introduction to Machine Learning.

This course introduces students to machine learning concepts and techniques for solving real-world prediction and pattern recognition problems. Topics include supervised learning methods such as regression, classification, decision trees, random forests, support vector machines, and neural networks, as well as unsupervised approaches including clustering and dimensionality reduction. Students will apply these methods using Python and common machine learning libraries to work with realistic datasets, emphasizing data preprocessing, model selection, and performance evaluation. At the end of the course, students will be able to explain core machine learning tasks, apply appropriate algorithms to new problems, and evaluate the effectiveness and limitations of different machine learning models in practical contexts. Prerequisite: CS 3358 and MATH 3305 with grades of "C" or better.

CS 4350. Unix Systems Programming.

This course examines systems programming on Unix/Linux platforms with an emphasis on low-level software development using C and modern system interfaces. Topics include Unix file system and environment, memory management, processes and signals, threads and synchronization, interprocess communication, device interaction, network programming, and secure systems programming, along with contemporary topics including multithreading scalability, containerization, and performance profiling. By the end of the course, students will be able to design and implement efficient and secure system software, analyze system behavior and performance, and apply Unix/Linux programming concepts to real-world computing environments. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4355. Algorithms and Analysis.

This course examines foundational and advanced algorithms in computer science with an emphasis on algorithm design, analysis, and problem formulation. Topics include techniques such as divide-and-conquer, greedy methods, dynamic programming, and backtracking, along with advanced tree, graph, sorting, searching, and string processing algorithms. Students develop skills in evaluating algorithm correctness, efficiency, and space usage using asymptotic analysis. Through analytical reasoning and implementation-based exercises, students learn to select and apply appropriate algorithmic strategies to solve non-trivial computational problems. Prerequisite: CS 3358 with grade of "C" or better.

CS 4371. Computer System Security.

This course examines the theoretical and applied principles of computer system security, including policy frameworks, cryptographic mechanisms, and offensive and defensive methodologies. Topics include security models, industry-standard practices, system vulnerabilities, and strategies for their mitigation. Students analyze secure network architectures, evaluate protective protocols, and investigate attack techniques that exploit system weaknesses. Through hands-on application and critical analysis, students develop the skills necessary to assess risk, respond to threats, and maintain the integrity of computing environments. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4372. Introduction to Digital Multimedia.

This course introduces the fundamental concepts and techniques of digital multimedia, focusing on how text, audio, images, and video are represented, processed, and compressed. Students examine multimedia systems and applications, multimedia transmission, and the role of standards in enabling interoperability and efficient media exchange. Topics include graphics and image representation, image and video processing, visual perception and color models, lossless and lossy compression (including frequency-domain methods), multimedia compression standards, and emerging technologies such as virtual and augmented reality. Prerequisite: CS 3358 with a grade of "C" of better.

CS 4373. Introduction to Network Science.

This course explores the fundamental concepts and algorithms of network science, including essential topics such as graph representations, centrality measures, link analysis, and community detection. Through a combination of theoretical study and computational projects, students will apply discrete mathematics and programming skills to model, visualize, and analyze real-world network datasets. By the end of the course, students should possess the analytical toolkit necessary to interpret large-scale network-structured data in the fields of data science and social computing. Prerequisite: CS 3358 with a grade of "C" or better.

CS 4379F. Distributed Data Processing.

This course introduces the principles and practice of distributed data processing for large-scale data-intensive applications. It covers core concepts such as distributed database architectures, distributed file systems, parallel processing models (e.g., MapReduce), and data management tools within the Hadoop ecosystem, including Hive, Pig, and HBase, as well as modern frameworks such as Apache Spark. The course emphasizes both foundational concepts and evolving technologies that support scalable data analytics across clusters. Instruction combines lectures with hands-on programming assignments and projects using Hadoop and related tools to reinforce practical skills. By the end of the course, students will be able to design, implement, and evaluate distributed data processing pipelines and reason about performance, scalability, and system trade-offs. Prerequisite: CS3354 and CS 3358 with grades of "C" or better.

CS 4379G. Data Analysis and Visualization.

This course introduces fundamental and practical techniques for data analysis and visualization in Python. Students learn data wrangling, cleaning, exploratory data analysis (EDA), unsupervised learning, and the construction of interactive dashboards. The methodology emphasizes hands-on programming, use of modern data science libraries, and open-source dashboard frameworks. Through assignments and a milestone-based project, students develop reproducible data analysis pipelines and communicate results visually. The course focuses on analytical methods, data processing techniques, and visual representation of data. Prerequisite: CS 2308 with grade of "C" or better.

CS 4379H. Cryptography.

This course introduces the fundamental paradigms and principles of modern cryptography, with emphasis on formal definitions, mathematical foundations, and algorithmic constructions. Topics include symmetric and public‑key cryptographic primitives, hash functions, digital signatures, and message authentication codes for secure systems. Students examine commonly defined security properties, the formal models used to specify them, and standard techniques for evaluating whether cryptographic constructions satisfy these properties. The course emphasizes rigorous reasoning using algebraic and number‑theoretic tools and introduces basic cryptanalysis methods for assessing algorithmic strength. By the end of the course, students will be prepared to read and interpret a substantial portion of contemporary cryptography research literature and widely used technical standards. Prerequisite: CS 3358 with grade of "C" or better.

CS 4379K. Introduction to Autonomous Robotics.

This course introduces programming, algorithms, and artificial intelligence techniques used in autonomous robotic systems. Topics include motion control, state estimation using Kalman filters, localization with particle filters, computer vision, object detection, task and motion planning, deep reinforcement learning, and multirobot coordination. The course also examines applications such as autonomous vehicles and analyzes the technical and societal considerations associated with intelligent robotic systems. Emphasis is placed on understanding algorithmic foundations, system integration, and practical implementation in real‑world robotic platforms. Prerequisite: CS 3358 with grade of "C" or better.

CS 4379Q. Introduction to Recommender Systems.

This course introduces students to the concepts and techniques of recommender systems for delivering personalized content, products, and information in modern, AI-driven applications. Major recommendation approaches are covered, including content-based filtering, neighborhood- and model-based collaborative filtering, matrix factorization, sequence-aware and context-aware methods, and hybrid architectures used in contemporary web, mobile, and streaming platforms. Students implement complete recommendation pipelines in Python using real interaction and rating data, with emphasis on data preprocessing, candidate generation, ranking, offline evaluation, and the design of A/B experiments and online evaluation strategies. By the end of the course, students are able to apply state-of-the-art recommendation algorithms, analyze and explain their performance using appropriate metrics, and critically evaluate issues such as privacy, fairness, transparency, and emerging regulatory and platform policies that shape recommender systems in today’s digital ecosystem. Prerequisite: CS 4347 with a grade of "C" or better.

CS 4379Y. Introduction to Green Computing.

This course introduces undergraduate students to the principles, methods, and technologies used to analyze and improve the energy efficiency of computing systems. Topics include energy‑efficient hardware design, software techniques for reducing power consumption, energy and carbon‑aware scheduling, resource management, and power measurement and profiling. Students examine data center efficiency metrics, trade‑offs between performance and energy use, and the computational characteristics of machine learning workloads. Emphasis is placed on quantitative evaluation, system‑level analysis, and evidence‑based comparison of design alternatives. The course treats sustainability‑related considerations as technical and economic constraints within computing systems, enabling students to assess design decisions using established metrics and tools rather than prescriptive policy positions. Prerequisite: [CS 2325 or CS 3339] and CS 3358 with grades of "C" or better.

CS 4380. Parallel Programming.

This course explores practical aspects of parallel programming, including multi-core processors and shared-memory programming, GPUs and accelerator programming, and distributed-memory machines and message-passing programming. Topics include MPI, POSIX threads, OpenMP, CUDA, HIP, loop parallelization, parallel algorithms, amorphous data parallelism, atomic operations, prefix sums, performance measurement, parallelism bugs, and case studies of parallel programs. Students gain applied knowledge and skills by developing, testing, and evaluating the performance of parallel software on various shared- and distributed-memory systems. Prerequisite: [CS 2325 or CS 3339] and CS 3360 with grades of "C" or better.

CS 4381. Practical Game Development.

This course explores the design and implementation of computer games through an in-depth study of game engine architecture, physics simulation, software design patterns, and artificial intelligence techniques. Students examine topics such as hierarchical state machines, genetic algorithms, state-space search algorithms, A* pathfinding, goal-based planning, and fuzzy logic, focusing on how these methods create responsive and believable game behavior. The course also emphasizes the integration of graphics engines, physics systems, and AI components to form cohesive game architectures, applying key software design principles such as modularity, scalability, and maintainability. Prerequisite: CS 3398 with a grade of "C" or better.

CS 4388. Computer Graphics.

This course examines the fundamental concepts and algorithms of computer graphics, including geometric transformations, scene representation, lighting and shading models, texture mapping, and animation. Students analyze mathematical and computational techniques used to construct and manipulate 2D and 3D graphical content. Topics include rendering pipelines, projection methods, and real-time visualization. Emphasis is placed on the implementation and evaluation of interactive graphics systems and the representation of visual data in digital environments. The course provides a foundation for studying modern graphics technologies and their applications in computing. Prerequisite: CS 3358 and [MATH 1317 or MATH 2321 or MATH 2417 or MATH 2471 or MATH 2472] with grades of "C" or better.

CS 4395. Independent Study in Computer Science.

This course allows interested undergraduate students to pursue an individual independent study under the supervision of a computer science faculty member. The course content depends on the chosen project but typically includes an in-depth study of the selected topic, a literature review, the implementation of an idea, the evaluation of said idea, and the presentation and a write-up of the project and its results. The selected topic must not already be covered in substantial detail in any other course offered by the Department of Computer Science. Prerequisite: Instructor approval.

CS 4398. Software Engineering Project.

This course serves as an undergraduate-level capstone experience in software engineering, with a focus on model-driven development and object-oriented methods. Students work in teams to design, implement, verify, and validate a small but complete software system. The course integrates core topics including UML-based modeling, agile processes such as eXtreme Programming, requirements specification through use cases, dynamic and statechart modeling, and the Model-View-Controller architecture. Emphasis is placed on systematic test case derivation and automated regression testing using JUnit. Students also gain hands-on experience with version control via Git and apply structured, team-based development practices to manage complexity, coordinate activities, and deliver a high-quality software product within realistic project constraints. Prerequisite: CS 3398 with a grade of "C" or better.

CS 5100. Advanced Computer Science Internship.

This course provides CS master’s students on-the-job training supervised by computer professionals in industry internship programs. During the internship, students apply what they have learned in the classroom toward the internship work. They are required to submit a mid-term and a final report of their internship work, describing what they have learned as a result of the internship and what curriculum additions or improvements they would suggest as a result of the internship experience. Students need the approval of the department in advance to take this internship course. Prerequisite: Instructor approval.

CS 5199B. Thesis.

This course provides continued enrollment for graduates engaged in thesis research and writing in computer science. Work is conducted under the direct supervision of a thesis advisor and involves activities necessary for completing the thesis, such as data collection, analysis, preparation of written dissertation chapters, and oral defense of the thesis. Candidates may participate in systems implementation, computational research, software engineering, or other approved investigative approaches as appropriate to their study. Enrollment may be needed for each long semester while conducting research or writing to maintain steady progress until the thesis is submitted for binding.

CS 5299B. Thesis.

This course provides continued enrollment for graduates engaged in thesis research and writing in computer science. Work is conducted under the direct supervision of a thesis advisor and involves activities necessary for completing the thesis, such as data collection, analysis, preparation of written dissertation chapters, and oral defense of the thesis. Candidates may participate in systems implementation, computational research, software engineering, or other approved investigative approaches as appropriate to their study. Enrollment may be needed for each long semester while conducting research or writing to maintain steady progress until the thesis is submitted for binding.

CS 5300. Professional Development of Graduate Assistants.

This course develops the professional, pedagogical, and technical skills required of master’s-level graduate instructional and teaching assistants in Computer Science. Through weekly seminars, guest speakers, and structured practice, students explore effective teaching strategies for lower-division CS courses, ethical and legal responsibilities, classroom and laboratory management, and core technical support skills relevant to department labs. Activities such as a formal teaching presentation, peer feedback, and written reflections emphasize clear communication, professionalism, and effective support of undergraduate learners. This course does not earn graduate degree credit.

CS 5301. Programming Practicum.

This course introduces graduate students to foundational programming concepts and practices for computing coursework. Topics include procedural and object-oriented programming, problem decomposition, program design, testing, debugging, and implementation in C++, with selected programming idioms from Java and Python. Students analyze and implement core data structures such as arrays, linked lists, stacks, queues, trees, and graphs, along with basic algorithms including searching, sorting, recursion, depth-first search, and breadth-first search. The course also examines contemporary programming workflows involving AI-assisted coding, code review, debugging, and responsible evaluation of AI-generated code.

CS 5302. Foundations of Data Structures and Algorithm Design.

This course examines classic data structures and the analysis of related algorithms through the lens of abstract data types. Students analyze the behavior of fundamental structures, including lists, stacks, queues, trees, graphs, and hash tables, while evaluating their impact on algorithmic performance regarding time and space complexity. Learners implement recursion and its various applications alongside elementary algorithms for sorting, searching, and hashing. Participants evaluate the trade-offs between dynamic and array-based implementations to determine appropriate structures for specific computational problems. By examining generic programming and heap structures, the course provides a comprehensive foundation for advanced programming and algorithm design.

CS 5303. Foundations of Computer Architecture.

This course provides foundational instruction in computer architecture for graduate students requiring reinforcement of core concepts. Topics include arithmetic logic units, instruction set architectures, datapath and control design, pipelining, multiprocessing, input/output systems, memory hierarchies, virtual memory, low level programming techniques, and performance evaluation. Students examine architectural tradeoffs through analytical methods and quantitative performance analysis. Emphasis is placed on understanding how hardware structures implement computation and influence system behavior.

CS 5305. Foundations of Operating Systems.

This course serves as a foundation course for computer science master's students who need reinforcement of fundamental concepts covered by CS 4328. The course examines the principles and design of modern operating systems. Topics include process and thread management, CPU scheduling, synchronization, interprocess communication, deadlocks, memory management, virtual memory, file systems, I/O, virtualization and cloud computing. Students explore system calls, kernel structure, and security mechanisms. Emphasis is placed on the interaction between hardware and software and on performance trade-offs. Programming assignments provide hands-on experience with system-level concepts using a Unix/Linux environment.

CS 5306. Advanced Operating Systems.

This course examines the principles and design of modern operating systems. Topics include process and thread management, CPU scheduling, synchronization, interprocess communications, deadlocks, memory management, virtual memory, file systems, I/O, virtualization and cloud computing. Students explore system calls, kernel structure, and security mechanisms. Emphasis is placed on the interaction between hardware and software and on performance trade-offs. Programming assignments provide hands-on experience with system-level concepts using a Unix/Linux environment.

CS 5310. Network and Communication Systems.

This course provides a study of network and communication systems. The course consists of three parts: foundations of data communications, essentials of computer networks and protocols, TCP/IP programming. Topics for data communications include types of networks, data types and their properties, network and application quality of services, modulations and multiplexing, and issues of signal transmissions. The computer networks and protocol part covers the core elements of computer networks and protocols such as sliding window protocols, LANs, Internet and TCP/IP. The programming part introduces TCP/IP networking socket APIs.

CS 5313. Machine Learning and Applications.

This course provides a rigorous technical foundation in machine learning theory, algorithms, and real-world applications for graduate students. It covers supervised and unsupervised learning, high-dimensional statistical modeling, and advanced ML/AI topics, including ensemble methods and generative and discriminative models. Students learn to address regression and classification tasks using data-driven paradigms, including clustering, ensemble learning, and dimensionality reduction, with emphasis on mathematical derivations, algorithmic implementation, and challenges such as the curse of dimensionality. Methodology emphasizes systematic experimentation, optimization, and critical evaluation using modern machine learning tools and reproducible workflows. Students complete a domain-driven project that integrates full data science pipelines with novel or advanced modeling approaches in domains such as healthcare, finance, and vision.

CS 5315. Responsible and Trustworthy AI.

This course examines the foundational principles and practices associated with responsible and trustworthy Artificial Intelligence (AI), introducing AI Engineering and approaches to developing AI systems with attention to reliability and risk. Topics include robustness, explainability, privacy, fairness, bias, and the use of generative AI models and machine learning in production environments. Students analyze the benefits, limitations, and trade-offs of these concepts and their integration into AI development. The course also reviews recent advancements and examines technical, regulatory, and ethical challenges within the AI domain. Prerequisite: CS 5313 with a grade of "C" or better.

CS 5316. Data Mining.

This course examines fundamental concepts, core methodologies, and recent developments in data mining. Typical topics include but are not limited to classification, cluster analysis, frequent pattern mining and related approaches for discovering structure and patterns in large‑scale data sets. Relevant research training and practice opportunities are also provided. Through programming assignments and projects, students will enhance their understanding of theoretical concepts and gain hands-on experience on practical techniques. They will also explore popular data mining tool packages such as Weka, Orange, KNIME and RapidMiner.

CS 5318. Principles of Programming Languages.

This course focuses on the principles of programming languages. Topics covered include programming paradigms, concepts of programming languages, formal syntax and semantics, and language implementation issues. Students examine principles used in specifying, designing, and implementing programming languages and review major paradigms including imperative, object-oriented, functional, logic, and concurrent programming. The course also explores how language features influence software development and implementation.

CS 5325. Reinforcement Learning.

This course examines the foundational principles and modern methods of reinforcement learning, in which agents learn sequential decision-making through interaction with an environment based on reward signals. Topics include Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, policy gradient methods, and deep reinforcement learning. Students investigate algorithms such as Q-learning, SARSA, actor-critic architectures, and proximal policy optimization, and explore applications including game-playing agents, robotics, and reinforcement learning from human feedback. Emphasis is placed on both theoretical analysis and practical implementation using Python, PyTorch, and OpenAI Gymnasium. Prerequisite: CS 5313 with a grade of "C" or better.

CS 5329. Algorithm Design and Analysis.

This course examines advanced algorithm design principles and computational complexity. Essential topics include core design techniques (such as divide-and-conquer, dynamic programming, and greedy methods), advanced algorithms for sorting, searching, and graphs, and the theory of NP-completeness. Students complete theoretical problem-solving and applied programming assignments to analyze the time and space complexity of algorithms and verify their correctness. Through these activities, students develop the advanced algorithmic toolkit necessary to design highly efficient and scalable solutions for a wide range of complex computational problems.

CS 5331. Crafting Compilers.

This course provides a study of the design and implementation of modern compilers, with an emphasis on foundational principles and practical system construction. It covers the compilation pipeline, including lexical analysis, parsing, semantic analysis, and code generation. Students also learn about various optimization techniques. The course also introduces students to the challenges in compiling for modern architectures and the role of compilers in enabling high-performance execution. Through a sequence of incrementally structured projects, students build a compiler from the ground up, where each stage extends prior components to form a complete system, providing hands-on experience in constructing complex software systems and understanding how compilers translate high-level abstractions into efficient machine-level execution.

CS 5332. Database Theory and Design.

This course examines the organization and management of data using relational database systems. Topics include data modeling, the Entity-Relationship model, and translating application data requirements into well-structured relational schemas. Fundamental design principles such as functional dependencies, normalization, and integrity constraints are introduced. Students gain practical experience creating databases and retrieving data through interactive SQL queries and programmatic access. Relational algebra, which provides the formal foundation for SQL, is also covered, along with underlying DBMS implementation technologies and advanced non-relational data models.

CS 5334. Advanced Internet Information Processing.

This course integrates web programming languages and data storage techniques for advanced information processing in Internet applications. Topics include database and big data support, as well as application-specific information processing algorithms. Students examine methods for building web-based information processing systems using languages such as PHP, Java, Java Servlets/JSP, and Python. The course also covers design considerations, system integration, and performance aspects of web applications, with practical implementation exercises to support applied learning.

CS 5338. Formal Languages.

This course covers the fundamental concepts and advanced topics in formal languages and automata theory. Through a combination of theoretical study and practical projects, students will learn how to analyze formal languages such as regular, context free, decidable and semi-decidable languages. Students will also learn how to create formal grammars such as regular and context free grammars, and design abstract machines such as finite state machines, pushdown automata and Turing machines. Other topics include Church-Turing thesis, Halting problem, reduction, computability and complexity.

CS 5341. Advanced Network Programming.

This course covers some of the advanced concepts and programming skills in computer networks. The course looks into the details of the TCP/IP protocol suite, in particular, the IP, TCP, and UDP protocols. Students will learn TCP/IP based programming skills based on socket API. Main topics covered include advanced TCP/IP, API, multicasting and broadcasting, reliable communications, advanced I/O functions and options. The course extends the programming part of CS 5310 and gets students prepared for more advanced TCP/IP network programming.

CS 5342. Robotics and Autonomous Systems.

This course provides a technical study of robotics and autonomous systems with an emphasis on algorithmic foundations and system implementation. Topics include robot kinematics, feedback control, probabilistic state estimation, perception using deep learning, motion planning, and coordination in multi-robot systems. Students evaluate real‑world applications, including autonomous vehicles, and assess documented system‑level impacts using empirical and scholarly sources. The course emphasizes hands-on development and testing of robotic systems through project-based learning, simulation environments, and real robots. Prerequisite: CS 5329 with grade of "C" or better.

CS 5343. Wireless Communications and Networks.

This course covers the fundamental aspects of wireless communications and wireless and mobile networks. Topics include the electromagnetic spectrum and propagation modes; antenna properties such as radiation patterns, gain, and loss; and key challenges in wireless communication, including interference and fading. The course also examines signal encoding and digitization techniques such as pulse code modulation and delta modulation, as well as the principles, advantages, and limitations of spread spectrum methods. Cellular network design is addressed, including architecture, operation, and handoff management.

CS 5346. Advanced Artificial Intelligence.

This course provides an introduction to Artificial Intelligence (AI) and generative AI, focusing on the analysis, design, implementation, and evaluation of AI systems. Topics include machine learning, knowledge representation, intelligent search, probabilistic reasoning, natural language processing, planning and decision-making, adversarial search, and generative models such as large language models, GANs, and VAEs. Students examine methods for developing and evaluating AI systems using established performance metrics and experimental methodologies. The course also addresses scalability, knowledge-based systems, and the analysis of technical, societal, and reliability considerations associated with AI systems.

CS 5351. Parallel Processing.

This course explores practical aspects of parallel processing, including multi-core CPUs and shared-memory programming, GPUs and accelerator programming, and distributed-memory computers and message-passing programming. The lectures cover MPI, POSIX threads, OpenMP, CUDA, HIP, loop parallelization, parallel algorithms, amorphous data parallelism, atomic operations, prefix sums, performance measurement, parallelism bugs, and case studies of parallel programs. Students are given opportunities to gain applied knowledge and skills by developing, testing, and evaluating the performance of parallel software on various shared- and distributed-memory systems.

CS 5352. Distributed Computing.

This course examines essential aspects of distributed and cloud computing. Topics include the history and evolution of distributed systems, architectures and models, system transparency, distributed time and clock synchronization, global states, and safety and fairness properties. The course also covers inter-process communication, concurrency control and atomicity, failure detection and recovery, fault tolerance including Byzantine failures, distributed consensus and coordination, remote method invocation, naming, and security. Students also examine cloud application development and case studies of distributed and cloud-based systems.

CS 5361. Generative Artificial Intelligence.

This course introduces generative artificial intelligence, focusing on models such as autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion-based methods. Topics include text, image, and audio generation, as well as applications such as data augmentation and synthetic data generation. Students implement and evaluate generative models through practical assignments. The course also examines technical considerations related to bias, synthetic media, and intellectual property, along with methods used to assess generated outputs across different application contexts. Prerequisite: CS 5313 with a grade of "C" or better.

CS 5369A. Data Science And Visualization.

This course introduces fundamental and practical techniques for data science and visualization in Python, with a focus on end-to-end analytics pipelines. Topics include data wrangling, data cleaning, exploratory data analysis, unsupervised learning, and the design of interactive dashboards. Students work with multi-source datasets and implement reproducible workflows using contemporary data science libraries, version control, and dashboard frameworks. The course also examines pipeline design, data representation, and visualization techniques, along with introductory concepts in environment management and workflow organization. Consideration is given to how design and infrastructure choices relate to interpretability, reliability, and downstream analytical applications.

CS 5369Q. Recommender Systems.

This course provides an advanced graduate-level treatment of recommender systems, focusing on algorithms and research methods for personalized content and decision support. Topics include content-based and collaborative filtering, matrix factorization, sequence- and context-aware models, and hybrid architectures used in large-scale environments. Students design and implement recommendation pipelines in Python using interaction data, with emphasis on candidate generation, ranking, offline evaluation metrics, and analysis of online experiments such as A/B tests. The course also examines methods for evaluating system performance and analyzing the effects of design choices in recommender systems.

CS 5369Y. Green Computing.

This course provides a graduate-level introduction to computing approaches related to energy efficiency and resource usage. Topics include energy-efficient hardware and system design, software optimization techniques, and methods for analyzing energy consumption in computing systems. The course examines data center efficiency, resource management strategies, and scheduling approaches based on energy-related constraints. Students use measurement and profiling tools to evaluate system performance and energy usage. Case studies are used to analyze trade-offs among performance, resource utilization, and system design choices.

CS 5375. Multimedia Computing.

This course introduces the fundamental concepts and techniques of digital multimedia. It covers the representation and compression of text, audio, images, and video, along with multimedia systems, applications, transmission, and the role of standardization in ensuring interoperability and efficient media exchange. Topics include multimedia systems and applications; graphics and image representation; image and video processing; visual perception and color models; lossless and lossy compression, including frequency-domain concepts; multimedia compression standards; and emerging technologies such as virtual and augmented reality.

CS 5378. Advanced Computer Security.

This course examines advanced concepts in computer security, from abstract security policies to the design and analysis of secure systems and networks. Topics include security models, cryptographic techniques, common vulnerabilities in modern systems, and established security practices. Students analyze how attacks are conducted, how vulnerabilities are identified, and how defensive mechanisms are implemented and evaluated. The course also introduces methods for assessing emerging security challenges and reviewing current research in computer security. Emphasis is placed on understanding system security from both theoretical and practical perspectives.

CS 5388. Advanced Computer Graphics.

This course examines the fundamental concepts and algorithms of computer graphics with an emphasis on advanced analysis and implementation. Topics include geometric transformations, scene representation, lighting and shading models, texture mapping, and animation. Students analyze and design complex graphics systems through the application of mathematical and computational techniques. Additional emphasis is placed on performance considerations, system-level design, and evaluation of rendering pipelines and visual representations. The course includes topics related to real-time graphics and modern visual computing systems.

CS 5391. Survey of Software Engineering.

This course examines the software development life cycle with an emphasis on system analysis, design, and implementation practices used in contemporary software engineering. Topics include requirements engineering, architectural design, development methodologies based on data‑flow and object‑oriented models, and techniques for verification and validation. The course also introduces professional standards and ethical frameworks relevant to software engineering practice, treating ethical issues as objects of analysis rather than prescriptive rules. Students engage with both industry practices and research‑informed approaches to understand tradeoffs, design decisions, and evaluation methods used in modern software systems.

CS 5392. Formal Methods in Software Engineering.

This course examines formal methods in software engineering with a focus on static program analysis and verification techniques. Topics include propositional and first-order logic, soundness and completeness, computability concepts, and formal proof methods. The course covers model checking, temporal logics such as linear time temporal logic (LTL) and computation tree logic (CTL), and specification of system properties including safety and liveness. Additional topics include satisfiability-based verification, symbolic analysis, and approaches to analyzing concurrent systems. Students engage with tools and methods used to evaluate software correctness and analyze the capabilities and limitations of formal verification techniques. Prerequisite: CS 5391 with a grade of "C" or better.

CS 5393. Software Quality.

This course examines dynamic program analysis algorithms and their role in software quality assessment. Topics include software correctness, reliability, and robustness; testing methodologies such as unit, integration, and regression testing; and techniques including mutation testing, data flow analysis, and symbolic evaluation. The course also covers dynamic validation approaches, dependency analysis, and challenges in object-oriented testing. Students analyze quality assurance strategies and apply formal and empirical methods to evaluate software systems. Prerequisite: CS 5391 with a grade of "C" or better.

CS 5394. Advanced Software Engineering Project.

This course serves as a graduate-level capstone experience in software engineering. Students work in teams to design, implement, verify, and validate a software system of substantial scope. The course integrates concepts from the software engineering curriculum, including requirements analysis, architectural design, process modeling, and verification and validation. Emphasis is placed on structured, team-based development using contemporary development approaches aligned with established software architectures and processes. Students apply systematic methods to manage complexity, coordinate team activities, and evaluate software quality within realistic project constraints. Prerequisite: CS 5391 with a grade of "C" or better.

CS 5395. Independent Study in Advanced Computer Science.

This course provides graduate students with the opportunity to pursue an individualized area of advanced study in computer science not covered by the existing curriculum. Students engage in independent work under faculty supervision, focusing on advanced topics or research-oriented study. The course includes investigation of a defined topic and may involve implementation, analysis, or theoretical exploration. Students present their work in an oral presentation addressing the central topic of study. Prerequisite: Instructor Approval.

CS 5396. Advanced Software Engineering Processes and Methods.

This course examines structured software engineering processes and methods, including object-oriented, aspect-oriented, feature-oriented, Cleanroom, PSP, TSP, Scrum, XP, and related approaches. Topics include process selection, software quality considerations, and the relationship between system requirements and development methodologies. The course also addresses tools and techniques for developing complex software systems, including interactive, mobile, and distributed applications. Students review literature on software engineering processes, methods, and tools, and analyze approaches to integrating process models with contemporary software development practices, including automated and agent-assisted development. Prerequisite: CS 5391 with a grade of "C" or better.

CS 5399A. Thesis A.

This course represents a student's initial thesis enrollment for graduate students engaged in thesis research and development in computer science. Work is conducted under the supervision of a thesis advisor and involves activities necessary for completing the thesis, such as data collection, analysis, and preparation of written dissertation chapters. Students may engage in systems implementation, computational research, software engineering, or other approved investigative approaches appropriate to their area of study.

CS 5399B. Thesis B.

This course provides continued enrollment for graduate students engaged in thesis research and writing in computer science. Work is conducted under the supervision of a thesis advisor and involves activities necessary for completing the thesis, including data collection, analysis, preparation of written dissertation chapters, and oral defense. Students may engage in systems implementation, computational research, software engineering, or other approved investigative approaches appropriate to their area of study.

CS 5599B. Thesis B.

This course provides continued enrollment for graduate students engaged in thesis research and writing in computer science. Work is conducted under the supervision of a thesis advisor and involves activities necessary for completing the thesis, including data collection, analysis, preparation of written dissertation chapters, and oral defense. Students may engage in systems implementation, computational research, software engineering, or other approved investigative approaches appropriate to their area of study.

CS 5999B. Thesis B.

This course provides continued enrollment for graduate students engaged in thesis research and writing in computer science. Work is conducted under the supervision of a thesis advisor and involves activities necessary for completing the thesis, including data collection, analysis, preparation of written dissertation chapters, and oral defense. Students may engage in systems implementation, computational research, software engineering, or other approved investigative approaches appropriate to their area of study.

CS 7100. Graduate Computer Science Internship.

This course provides doctoral students in computer science with supervised industry internship experience. Students engage in professional activities under the guidance of computer scientists or engineers in an organizational setting. The internship involves applying computing knowledge to practical tasks and includes documentation of work through structured reports. The course focuses on the integration of academic knowledge with professional practice in computing environments.

CS 7199. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and complete activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Candidates may employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials associated with dissertation work. Prerequisite: Instructor approval.

CS 7299. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and complete activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Candidates may employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials associated with dissertation work. Prerequisite: Instructor approval.

CS 7300. Introduction to Research in Computer Science.

This course introduces foundational concepts and practices in computer science research. Topics include research processes, methodologies, ethics, institutional review considerations, literature review, paper critique, research proposal development, and presentation techniques. Students examine examples of faculty research to understand current research areas and available tools and platforms used in computing research. The course emphasizes analysis of research methods and communication of scholarly work.

CS 7309. Professional Development of Doctoral Assistants.

This course examines the roles and responsibilities of doctoral students serving as instructional and teaching assistants in computer science. Topics include pedagogy for introductory and upper-division courses, ethical and legal considerations, supervision and coordination of lab activities, and technical support practices in instructional settings. Students participate in seminars, guest presentations, and practice-based assignments, including teaching presentations, peer review, and reflective writing. The course addresses instructional methods, mentoring approaches, and professional conduct in academic environments.

CS 7311. Data-Driven AI Systems Design.

This course provides an advanced, research-oriented study of how data science drives the design, development, and evaluation of AI-powered systems. It focuses on end-to-end, data-centric workflows in which data informs task definition, model selection, system architecture, and evaluation protocols across diverse application domains. Students design and implement robust pipelines for collecting, cleaning, transforming, and integrating multi-modal data, and then use these pipelines to develop and assess AI methods under realistic constraints such as scale, noise, and drift. Methodology emphasizes reproducible experimentation, critical comparison of alternative data and model choices, and reflection on how data quality, bias, and feedback loops shape system behavior and downstream impacts. Students conduct original, data-driven research on AI systems and communicate design trade-offs and empirical findings to both technical and interdisciplinary audiences.

CS 7312. Advanced Data Mining.

This course provides in-depth coverage of data mining topics, including classification, cluster analysis, and frequent pattern mining. Students examine theoretical foundations and implement data mining techniques through programming assignments. The course includes the use of data mining tools and frameworks for analysis and experimentation. Students complete a project involving the application of data mining methods to a defined problem, including data preparation, model development, and evaluation.

CS 7313. Advanced Machine Learning and Pattern Recognition.

This course examines advanced theoretical concepts and methods in machine learning and pattern recognition. Topics include traditional algorithms such as support vector machines and ensemble methods, as well as deep learning architectures including convolutional networks, recurrent networks, and transformers. The course addresses feature engineering, model evaluation, and optimization techniques through algorithmic and computational approaches. Students analyze and implement machine learning methods and evaluate their performance across different problem settings.

CS 7314. Bioinformatics.

This course introduces algorithms and computational methods for data-intensive analysis in biological and biomedical applications, including drug response prediction, gene regulatory network analysis, and protein/RNA structure prediction. Topics include greedy algorithms, linear and statistical modeling, clustering, network analysis, expectation-maximization, and Hidden Markov models, as well as machine learning and deep learning approaches for high-dimensional biological data. The course examines integration of classical algorithms with data-driven modeling frameworks and methods for analyzing complex biological datasets.

CS 7315. Network Science.

This course examines theoretical foundations and current research topics in network science. Topics include mathematical models for complex networks, computational algorithms for structural analysis, dynamic processes on networks, and graph-based machine learning and data mining methods. Students analyze research literature and complete project-based assignments involving modeling, analysis, and algorithm design for networked systems.

CS 7321. Human Computer Interaction: Concepts, Models, and Methodologies.

This course explores advanced methods for designing, implementing, and evaluating interaction techniques in computing systems. Topics include input method design, performance evaluation, components of the interaction pipeline, and hardware and software considerations. The course also examines usability, privacy, and the application of artificial intelligence methods in human-computer interaction. Students develop and evaluate interaction techniques through implementation and testing, with attention to contextual and behavioral factors that influence system performance.

CS 7323. Image Processing and Computer Vision.

This course introduces fundamental and advanced concepts in image processing and computer vision. Topics include image formation, filtering, frequency domain, enhancement, feature extraction, segmentation, and object recognition. The course also covers modern approaches based on machine learning and deep learning for visual data analysis, with applications in areas such as autonomous systems, robotics, and video analytics. Emphasis is placed on both theoretical foundations and practical implementation of algorithms for analyzing and interpreting images and videos.

CS 7324. Advanced Multimedia Systems: Perception, Quality, and Immersive Media.

This course explores the foundations of modern multimedia systems with an emphasis on end-to-end Quality of Experience (QoE) in digital content delivery. Students study advanced methods for the representation, compression, processing, and transmission of multimedia, including immersive media such as augmented and virtual reality media. A central focus is the modeling and evaluation of user-perceived quality through principles from perceptual psychology and cognitive science. Topics include subjective quality assessment methodologies, objective quality metrics, and the analysis of system factors such as latency, jitter, and resolution, particularly in immersive environments.

CS 7331. High-Performance Computing.

This course examines the advanced design, analysis, and optimization of high-performance applications on modern computing systems. It covers key topics such as high-performance architectures (including accelerators and systems-on-chip), performance modeling and benchmarking, data and control dependence analysis, data locality, memory hierarchy management, techniques for exposing parallelism, and code transformations across diverse workloads. The course integrates theoretical foundations with analysis of applications and systems to address performance across hardware and software layers.

CS 7332. Advanced Parallel Computing.

This course explores advanced methods for designing, implementing, and evaluating parallel algorithms for shared-memory systems, including GPUs and multicore CPUs. Topics include algorithm design, performance optimization, parallelization techniques, parallel hardware, programming models, and language support for parallel programming. The course covers OpenMP, CUDA, HIP, synchronization primitives, amorphous data parallelism, prefix scans, cache coherence, memory consistency, implementation styles, and case studies of parallelizing complex algorithms. The course includes development and evaluation of parallel software for contemporary platforms, with emphasis on performance profiling and program instrumentation.

CS 7333. Advanced Green Computing.

This course covers advanced Ph.D.-level topics in green computing. Topics include hardware techniques for energy efficiency, software design approaches related to energy use, and methods for analyzing AI systems in relation to energy and resource consumption. The course examines data center efficiency, resource management, and scheduling strategies under energy and carbon-related constraints. It includes analysis of current research literature on energy-efficient computing systems. Students conduct research activities, including power measurement, profiling, and evaluation of computing systems using established methods.

CS 7334. Scalable High Performance Computing Systems.

This course introduces the principles and practice of developing scalable applications for high performance computing (HPC) systems, with an emphasis on distributed infrastructures. It covers distributed-memory parallel computing through message-passing paradigms, including communication, parallel I/O, and data access to storage systems. The course examines system-level abstractions such as parallel file systems and resilience mechanisms, including checkpointing, in relation to performance, scalability, and reliability. It includes analysis of research literature across application domains and the use of performance analysis tools to measure and model application behavior on large-scale computing platforms.

CS 7341. Cyberspace Security.

This course examines advanced principles, research methodologies, and emerging challenges in securing computing systems. Students analyze research literature that established core paradigms, including security models, cryptography, systems security, network security, privacy, and adversarial machine learning. The course emphasizes threat modeling, formal reasoning, and experimental evaluation. Through seminar discussions and a research project, students engage with ethical and societal considerations related to cybersecurity technologies and defenses.

CS 7343. Mobile Networks and Computing.

This course offers an in‑depth exploration of modern wireless and mobile communication networks, emphasizing both foundational principles and emerging technologies. Students examine wireless network measurements and modeling, channel assignment strategies, coverage planning, and the design of wireless network protocols. The course also addresses mobile data management and essential wireless security mechanisms. Applications across diverse wireless environments—such as ad hoc networks, sensor networks, delay‑tolerant networks, and mobile social networks—are studied to illustrate real‑world challenges and innovations in contemporary wireless systems.

CS 7344. Cryptography & Machine Learning for Cyber-Physical Systems Security.

This course introduces the fundamentals of cryptography and machine learning as applied to security and privacy in cyber-physical systems (CPS). Topics include CPS architectures, cryptographic techniques, machine learning algorithms, and security threats and attacks on CPS. The course also covers privacy-preserving machine learning methods and design principles for secure CPS. It examines how cryptographic and machine learning approaches are applied within CPS environments.

CS 7351. Advanced Software Engineering.

This course examines advanced concepts and techniques in software engineering, with an emphasis on automated software generation, analysis, and verification. Topics include software process programming, symbolic execution, model checking, property specification and checking, and runtime verification of complex software systems. The course also considers emerging directions at the intersection of software engineering and artificial intelligence, including software engineering for AI systems and the use of AI techniques to support software development, testing, and maintenance. Students analyze research-driven methods, evaluate their strengths and limitations, and apply formal and automated approaches to improve the reliability, quality, and maintainability of modern software systems.

CS 7352. Real-time Systems.

This course covers issues related to the design and analysis of systems with real-time constraints. It examines scheduling and synchronization mechanisms used to manage computing resources under timing requirements. Topics include real-time scheduling algorithms and synchronization protocols, along with analysis of research literature in real-time systems. The course addresses theoretical and practical aspects of ensuring temporal correctness in computing systems.

CS 7387. Research in Computer Science.

This course provides a faculty-guided independent research experience for doctoral students in computer science. Students conduct an in-depth investigation of a focused research topic, including evaluation of scholarly literature, formulation of research questions, and application of research methodologies. The course includes independent study and analysis related to a defined research area. Students present the results of their work in a formal public presentation. Prerequisite: Instructor approval.

CS 7389H. Advanced Deep Learning.

This course provides an in-depth exploration of deep learning, emphasizing multi-layer neural networks and their applications. Topics include convolutional, recurrent, and graph neural networks, optimization algorithms, and generative models. The course examines mathematical and computational methods for analyzing datasets in areas such as computer vision, natural language processing, audio analysis, and reinforcement learning. It includes design, implementation, and evaluation of deep learning architectures using contemporary frameworks.

CS 7389I. Extended Reality and Immersive User Interfaces.

This course provides an overview of extended reality (XR) technologies, software systems, immersive user interfaces, and spatial interaction techniques. Topics include the geometry of immersive interfaces, motion and physics in virtual environments, human visual perception, and design considerations for XR systems. The course also covers XR development across heterogeneous hardware platforms, 3D user interface prototyping, and methods for evaluating immersive systems using quantitative and qualitative approaches. It integrates concepts from computer graphics, human-computer interaction, and cognitive science within a multidisciplinary framework.

CS 7389J. Advanced Natural Language Processing.

This course examines core concepts, tasks, and techniques in contemporary Natural Language Processing, with emphasis on neural network–based approaches and large language models. Topics include text classification, multimodal modeling, and computational approaches to human behavior. Students analyze foundational principles, modern architectures, and applications across a range of NLP tasks. The course also addresses methods for collecting and annotating text data, as well as representations of linguistic structure. Emphasis is placed on the interplay among data, model design, and evaluation in current NLP systems.

CS 7389K. Advanced Robotics and Autonomous Systems.

This course examines advanced algorithms and methodologies used in contemporary robotics and autonomous systems research. Topics include motion control, state estimation using Kalman and particle filters, localization, computer vision, object detection, task and motion planning, deep reinforcement learning, and multirobot coordination. Students analyze and implement these techniques within robotic software frameworks and evaluate their performance in representative application domains such as autonomous vehicles and mobile robots. Emphasis is placed on understanding underlying mathematical models, algorithmic trade‑offs, and research methodologies relevant to advanced robotics systems.

CS 7399. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

CS 7599. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

CS 7699. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

CS 7999. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.