Master of Science (M.S.) Major in Computer Science (Thesis Option)

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 5326. Advanced Studies in Human Factors of Computer Science.

This course provides a professional-level presentation of techniques and research findings related to human-computer interactions.

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 5369L. Machine Learning and Applications.

Provides broad introduction to machine learning, including learning theory, and recent topics like support vector machines and feature selection. Covers basic ideas, intuition, and understanding behind modern machine learning methods. Discusses applications like face recognition, text recognition, biometrics, bioinformatics, and multimedia retrieval.

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.