COMP 1101 Analytical Inquiry I (4 Credits)

Students explore the use of mathematics and computer programming in creating animations. Students create animations on their laptop computers using animation software. This course counts toward the Analytical Inquiry: The Natural and Physical World requirement.

COMP 1201 Introduction to Computer Science I (2 Credits)

This course introduces the discipline of computer science and how it applies the natural and physical world and society. Topics include the history of computing, computer hardware components, the internet, ethics, and uses computation as a means to analyze, process, model, and understand our world. This course counts toward the Analytical Inquiry: The Natural and Physical World requirement. Ideally taken concurrently with COMP 1351.

COMP 1202 Introduction to Computer Science II (2 Credits)

This course continues the introduction of the discipline of computer science by exploring major areas within it. Topics covered include examples from data structures, algorithms, databases, programming languages, parallel computing, artificial intelligence, robotics, cyber-security, data science, gaming, and ethics. This course counts toward the Analytical Inquiry: The Natural and Physical World requirement. Prerequisite: COMP 1201.

COMP 1203 Introduction to Computer Science III (2 Credits)

This course introduces testing and software development in computer science. Topics covered include using debuggers, version control systems, unit testing and general testing, Unified Modeling Language (UML), computing ethics, and software development in a team setting. Prerequisite: COMP 1202.

COMP 1351 Introduction to Programming I (3 Credits)

This course is an introduction to fundamental aspects of computer programming. Topics covered include variables, conditional statements, iteration, functions, basic data structures, objects, file input/output and interactions. Satisfies 3 credits of Analytical Inquiry: Natural and Physical World.

COMP 1352 Introduction to Programming II (3 Credits)

This course continues to introduce more advanced programming topics using the Python programming language. Topics include classes, types, inheritance, methods/functions, testing, graphical-user interfaces, threads, data manipulation, functional programming, and recursion. This course counts toward the Analytical Inquiry: The Natural and Physical World requirement. Prerequisite: COMP 1351.

COMP 1353 Introduction to Data Structures & Algorithms I (3 Credits)

This course introduces data structures used in computation, including their behavior, usage, implementation, and the analysis of their space usage. In addition, the algorithms used for access, manipulation, and updating the data structures is covered. Data structures and algorithms addressed include contiguous and linked lists, stacks, queues, hash tables, heaps, trees, self-balancing trees, graphs, and graph traversal. Introductory runtime analysis is used to prove time and space requirements for data structures and their performance while being used. Prerequisite: COMP 1352.

COMP 1601 Computer Science Pathways (1 Credit)

This course is designed to help first year computer science and game development students succeed in a very challenging major. Topics and activities may include academic success strategies; personal inventory exercises; interviewing computer science alumni; exploring ethical concerns within the profession; seminars by industry and academic experts; establishing the relationships between computing and other disciplines; critical and creative thinking activities; disseminating information on the dual degree programs, the honors program requirements, the honor code, and computer science department program structures; and readings from and discussions about computing related articles and publications.

COMP 1671 Introduction to Computer Science I (4 Credits)

Characteristics of modern computers and their applications; analysis and solution of problems; structure programming techniques; introduction to classes, abstract data types and object-oriented programming. This course counts toward the Analytical Inquiry: The Natural and Physical World requirement. Prerequisite: high school algebra.

COMP 1672 Introduction to Computer Science II (4 Credits)

Advanced programming techniques; arrays, recursion, dynamic data structures, algorithm abstraction, object-oriented programming including inheritance and virtual functions. Prerequisite: COMP 1671.

COMP 1991 Independent Study (1 Credit)

COMP 2300 Discrete Structures in Computer Science (4 Credits)

Number systems and basic number theory, propositional and predicate logic, proof techniques, mathematical induction, sets, counting and discrete probability, case studies with applications from computer science, such as data representation, algorithm analysis and correctness, and system design. Prerequisites: COMP 1672 or COMP 1352.

COMP 2355 Intro to Systems Programming (4 Credits)

The prerequisites for this class are a good understanding of imperative and object-oriented programming in Java. The prerequisites for this class include a good understanding of basic programming constructs, such as branches (if, switch), loops (for, while, do), exceptions (throw, catch), functions, objects, classes, packages, primitive types (int, float, boolean), arrays, arithmetic expressions and boolean operations. Computer organization is a parallel prerequisite; if possible, students should register for both this course and COMP 2691. You must have a good understanding of basic data structures such as arrays, lists, sets, trees, graphs and hash-tables. This is a class on systems programming with focus on the C programming language and UNIX APIs. There will be programming assignments designed to make you use various Debian GNU/Linux system APIs. Programming assignments involve writing code in C or C++. Prerequisite: COMP 2673.

COMP 2361 Systems I (4 Credits)

This course introduces low-level computer systems and programming. Topics covered include Linux, the C programming language, pointers, dynamic memory management, number systems, instruction set architectures, debugging, and caching. Prerequisites: COMP 1353.

COMP 2362 Systems II (4 Credits)

This course introduces computer operating systems and parallel programming. Topics covered include processes, process forks, threads, race conditions, synchronization, scheduling, memory systems, resource sharing, and sockets. Prerequisite: COMP 2361.

COMP 2370 Introduction to Data Structures & Algorithms II (4 Credits)

This course introduces the performance analysis of algorithms, including proof techniques; data structures and their physical storage representation, including space and performance analysis; recursive techniques; stacks, queues, lists, trees, sets, graphs; sorting and searching algorithms. Prerequisites: MATH 2200 or COMP 2300; COMP 1353 or COMP 2673.

COMP 2381 Object-Oriented Software Development (4 Credits)

Some problems are most naturally modeled by a hierarchy of objects and the relationships between those objects. This course introduces object-oriented design and development as a problem solving technique. Topics covered include the Java programming language, including classes, methods, fields, inheritance, interfaces, polymorphism, generics, static typing, design patterns, and the Java Collections Framework. Prerequisite: COMP 1353.

COMP 2673 Introduction to Computer Science III (4 Credits)

An introduction to several advanced topics in computer science. Topics vary from year to year and may include any of the following: theory of computing, cryptography, databases, computer graphics, graph theory, game theory, fractals, mathematical programming, wavelets, file compression, computational biology, genetic algorithms, neural networks, simulation and queuing theory, randomized algorithms, parallel computing, complexity analysis, numerical methods. Prerequisite: COMP 1672 or COMP 1771.

COMP 2691 Introduction to Computer Organization (4 Credits)

This course covers basic topics in Computer Organization and is a required course in the BS in Computer Science, BS in Game Development, and BS in Computer Engineering degrees. Topics include: instruction set architectures, integer and floating point arithmetic, processors, memory systems, and topics in storage and Input/Output. Prerequisite: COMP 1672.

COMP 2701 Topics in Computer Science (1-5 Credits)

COMP 2821 Introductory Game Design (4 Credits)

Learn fundamental game design practices through the creation of paper and physical game prototypes using a play-centric design process. Topics include the formal elements, dramatic elements, and system dynamics of games, with an emphasis on playtesting, game analysis, and group game projects. Program Prerequisites: Restricted to Game Development and EDPX majors or instructor approval. Course prerequisites: COMP 1352 or EDPX 2100.

COMP 2901 Computing and Society (4 Credits)

This course is designed to explore the social implications of computing practices, organization and experience. These topics and other issues are correlated with examples from the older and modern history of technology and science. Some formal experience with computing is assumed, but students who have a good familiarity with ordinary computing practice should be ready. Students are also expected to contribute their expertise in one or more of the areas of their special interest. Cross listed with DMST 3901.

COMP 3000 Seminar: The Real World (1 Credit)

Series of lectures by alumni and others on surviving culture shock when leaving the University and entering the job world. Open to all students regardless of major. Cross listed with MATH 3000.

COMP 3009 Essential Math for Data Science and AI (4 Credits)

The primary objective of the course is to empower students with the mathematical tools most used in data science. Students will develop an understanding of fundamental topics such as matrix operations, eigenvectors, and singular value decomposition (SVD), along with key calculus principles, including derivatives and integrals, with a focus on their applications in data science. The course also explores elements of multivariate calculus and optimization techniques to solve real-world problems, as well as probability concepts relevant to modeling uncertainty and variability in data. Additionally, students will gain exposure to implementing mathematical models using Python libraries such as NumPy, Pandas, and Matplotlib to analyze and manipulate data. By synthesizing these mathematical principles, students will be prepared for more advanced topics in data science and machine learning. Graduate student will be expected to upload a course presentation/project assignment to their individual Github portfolio.

COMP 3100 Human-Computer Interaction (4 Credits)

Introduces students in computer science and other disciplines to principles of and research methods in human-computer interaction (HCI). HCI is an interdisciplinary area concerned with the study of interaction between humans and interactive computing systems. Research in HCI looks at cognitive and social phenomena surrounding human use of computers with the goal of understanding their impact and creating guidelines for the design and evaluation of software, interfaces, physical products, and services in industry. Prerequisite: COMP 1353, but students from all disciplines are welcome. Cross-listed with COMP 4100.

COMP 3200 Discrete Structures (4 Credits)

Discrete mathematical structures and non-numerical algorithms; graph theory, elements of probability, propositional calculus, Boolean algebras; emphasis on applications to computer science. Prerequisites: COMP 2300 and COMP 1353 for undergraduates and COMP 3004 for graduates.

COMP 3334 Parallel and Distributed Computing for Data Science and AI (4 Credits)

Current techniques for effective use of parallel processing and large-scale distributed systems for data science. Programming assignments will give students experience in the use of these techniques. Specific topics will vary from year to year to incorporate recent developments. This course is not to be used for the MS Computer Science. Graduate students are required to contribute at least one course assignment to their personal Github portfolio. Prerequisite: COMP 2370.

COMP 3351 Programming Languages (4 Credits)

Learn the fundamentals of programming languages through functional programming through an in-depth understanding of syntax and semantics around program structures and how programming languages are parsed and interpreted. Understand recursion as a fundamental problem-solving paradigm and the important role that higher order types and kinds play in eliminating errors and simplifying software development. Satisfies the Programming Language elective requirement. Prerequisites: COMP 2362 and COMP 2370.

COMP 3352 Elements of Compiler Design (4 Credits)

Techniques required to design and implement a compiler; topics include lexical analysis, grammars and parsers, type-checking, storage allocation and code generation. Prerequisite: COMP 3351.

COMP 3353 Compiler Construction (4 Credits)

Design and implementation of a major piece of software relevant to compilers. Prerequisite: COMP 3352.

COMP 3361 Operating Systems I (4 Credits)

Operating systems functions and concepts; processes, process communication, synchronization; processor allocation, memory management in multiprogramming, time sharing systems. Prerequisites: for undergraduates: (COMP 2355 and COMP 2691) or COMP 2361; COMP 2370; for graduate students: COMP 3003, 3004, and 3005.

COMP 3371 Data Structures & Algorithms (4 Credits)

Design and analysis of algorithms and data structures; asymptotic complexity, recurrence relations, lower bounds; algorithm design techniques such as incremental, divide-and-conquer, dynamic programming, iterative improvement, greedy algorithms; randomized data structures and algorithms. Prerequisites: COMP 2370 or equivalent and COMP 3200.

COMP 3372 Advanced Algorithms (4 Credits)

Advanced techniques for the design and analysis of algorithms and data structures; amortized complexity, self-adjusting data structures; randomized , online, and string algorithms; NP-completeness, approximation and exact exponential algorithms; flow networks.

COMP 3381 Software Engineering I (4 Credits)

An introduction to software engineering. Topics include software processes, requirements, design, development, validation and verification and project management. Cross-listed with COMP 4381. Prerequisites: COMP 3351; COMP 3361 or COMP 2362; or instructor permission.

COMP 3382 Software Engineering II (4 Credits)

Continuation of COMP 3381. Topics include component-based software engineering, model-driven architecture, and service-oriented architecture. Prerequisite: COMP 3381.

COMP 3384 Secure Software Engineering (4 Credits)

This course is concerned with systematic approaches for the design and implementation of secure software. While topics such as cryptography, networking, network protocols and large scale software development are touched upon, this is not a course on those topics. Instead, this course is on identification of potential threats and vulnerabilities early in the design cycle. The emphasis in this course is on methodologies and paradigms for identifying and avoiding security vulnerabilities, formally establishing the absence of vulnerabilities, and ways to avoid security holes in new software. There are programming assignments designed to make students practice and experience secure software design and development. Prerequisites: COMP 2362 or COMP 3361.

COMP 3410 Web Programming I (4 Credits)

This course will give an overview of machine learning techniques, their strengths and weaknesses, and the problems they are designed to solve. This will include the broad differences between supervised, unsupervised and reinforcement learning and associated learning problems such as classification and regression. Techniques covered, at the discretion of the instructor, may include approaches such as linear and logistic regression, neural networks, support vector machines, kNN, decision trees, random forests, Naive Bayes, EM, k-Means, and PCA. After taking the course, students will have a working knowledge of these approaches and experience applying them to learning problems. Enforced Prerequisites: COMP 1353, MATH 1952, and MATH 2060.

COMP 3411 Web Programming II (4 Credits)

In this course you will learn how to develop a full-stack web application that is capable of serving dynamic content from a database. Furthermore, you will learn the core design concepts and principles that will enable you to develop scalable and easy to maintain webapplications - a set of skills that will serve you well in both your personal and professional projects in the future. Prerequisite: COMP 3410.

COMP 3412 Web Projects: Web Development III (4 Credits)

In this course you will learn how to develop, as a group, a full-stack web application that is capable of serving dynamic content from a database. We will use the MongoDB, ExpressJS, Angular, and Node.js (MEAN) software stack to work on a real-life problem presented to us by an external product owner. In the class we will use the Scrum framework for Agile development to work, as a software team, through several sprints of development. You will be peer reviewing each other throughout the course, and the product owner will also be reviewing your product through end-of-sprint demos as features are completed. The goal for this class is for it to be a fun, collaborative, and educational environment that demonstrates what it is like to work as a real software team. Prerequisite: COMP 3411.

COMP 3421 Database Organization & Management I (4 Credits)

An introductory class in database management systems covering both relational and non-relational databases with an emphasis on relational. Topics include database design, ER modeling, relational algebra, SQL, scripting, and embedded SQL. Each student will design, load, query and update a nontrivial database using a relational database management system (RDBMS). In addition, an introduction to a NoSQL database will be included. Graduate students will read one or two relevant technical papers and write a summary report. Prerequisites: for undergraduates: COMP 1353 or COMP 2673; for graduates: COMP 3005 or COMP3006 (MS Data Science).

COMP 3424 NoSQL Databases (4 Credits)

In this course, students learn what NoSQL databases are, learn to identify the differences between them, and gain a fundamental understanding between SQL, relational databases, and NoSQL databases. Students further explore which type of NoSQL database is the correct one given a use-cases, examining types, methods of communicating with it, contrasts to other NoSQL databases, performance and scalability. Prerequisites: for undergraduates, COMP 2355 or COMP 2361; for graduates: COMP 3005.

COMP 3431 Data Mining (4 Credits)

Data Mining is the process of extracting useful information implicitly hidden in large databases. Various techniques from statistics and artificial intelligence are used here to discover hidden patterns in massive collections of data. This course is an introduction to these techniques and their underlying mathematical principles. Topics covered include: basic data analysis, frequent pattern mining, clustering, classification, and model assessment. Prerequisites: COMP 2370.

COMP 3432 Machine Learning (4 Credits)

This course will give an overview of machine learning techniques, their strengths and weaknesses, and the problems they are designed to solve. This will include the broad differences between supervised, unsupervised and reinforcement learning and associated learning problems such as classification and regression. Techniques covered, at the discretion of the instructor, may include approaches such as linear and logistic regression, neural networks, support vector machines, kNN, decision trees, random forests, Naive Bayes, EM, k-Means, and PCA. After taking the course, students will have a working knowledge of these approaches and experience applying them to learning problems. Enforced Prerequisites: COMP 1353, MATH 1952, and MATH 2060.

COMP 3433 Data Visualization (4 Credits)

This course explores visualization techniques and theory. The course covers how to use visualization tools to effectively present data as part of quantitative statements within a publication/report and as an interactive system. Both design principles (color, layout, scale, and psychology of vision) as well as technical visualization tools/languages will be covered. Prerequisites: COMP 1353 and Python Programming.

COMP 3441 Introduction to Probability and Statistics for Data Science & AI (4 Credits)

The course introduces fundamentals of probability for data science. Students survey data visualization methods and summary statistics, develop models for data, and apply statistical techniques to assess the validity of the models. The techniques will include parametric and nonparametric methods for parameter estimation and hypothesis testing for a single sample mean and two sample means, for proportions, and for simple linear regression. Students will acquire sound theoretical footing for the methods where practical, and will apply them to real-world data, primarily using R. Prerequisite: COMP 1353, MATH 1952, MATH 2060.

COMP 3442 Advanced Probability and Statistics for Data Science (4 Credits)

This course builds on material in Probability and Statistics 1. Students will carry out model fitting and diagnostics for multiple regression, ANOVA, ANCOVA, and generalized linear models. Dimension reductions techniques such as PCA and Lasso are introduced, as are techniques for handling dependent data. The course introduces the principles of resampling and Bayesian Analysis. Students will acquire sound theoretical footing for the methods where practical, and will apply them to real-world data, primarily using R. Prerequisite: COMP 3441.

COMP 3450 Machine Learning Operations (4 Credits)

This course introduces the engineering and deployment of machine learning systems. Students will learn essential technical and organizational skills needed to develop and deploy ML solutions, progressing from fundamental concepts through to practical implementation. Key topics include ML project lifecycle management, containerization, web application development, data management strategies, and cloud deployment. Focus will be given to the application of industry-standard tools and best practices for building production-ready machine learning systems. Prerequisites: COMP 3421 and COMP 3432.

COMP 3455 Shell Scripting and System Tools (4 Credits)

This course covers navigating and utilizing tools in a UNIX environment, including use of common command line utilities, Bash and Python shell scripting, source control via Git, pipes and I/O redirection, networking in Python and OS multi-processing/multi-threading. More emphasis will be placed on using these tools than on how those tools work. Students should have experience with Python prior to taking this course.

COMP 3456 Deep Learning for Sequence Data (4 Credits)

This course introduces modern approaches to analyzing sequential data, with a focus on deep learning methods for time series and natural language processing. Students will develop practical skills in building and implementing neural network models for sequential data analysis, while gaining a thorough understanding of the underlying concepts. Through hands-on projects and real-world applications, students will learn to process, analyze, and generate predictions from various types of sequential data. The course emphasizes practical implementation and modern deep learning frameworks, bridging the gap between traditional statistical methods and contemporary neural network approaches. Prerequisite: COMP 3432.

COMP 3490 Undestanding AI (4 Credits)

Understanding Artificial Intelligence provides a comprehensive introduction to artificial intelligence (AI) for students of all backgrounds. This course explores the diverse approaches to AI, including rule-based systems, expert systems, knowledge representation, search algorithms, machine learning, natural language processing, robotics, and human-AI interaction. Students will gain hands-on experience with AI tools and applications while critically examining their impact on society. The course emphasizes the capabilities, limitations, and ethical considerations of AI, preparing students to engage thoughtfully with AI technologies in various fields. Prerequisites: Non-majors only, python programming is helpful but not required.

COMP 3491 Applications of Generative AI (4 Credits)

Applications of Generative Artificial Intelligence is a hands-on course explores the transformative power of generative artificial intelligence (AI) across various industries. Students will engage directly with generative AI tools and techniques, including text generation, image synthesis, music composition, code generation, and more. Through practical exercises, projects, and case studies, students will develop a deep understanding of how generative AI can enhance creativity, automate workflows, and drive innovation in diverse fields. This course is designed for students who have completed COMP 3490 and want to apply AI in practical, industry-relevant contexts. Prerequisite: COMP 3490 and non-majors.

COMP 3495 AI Design Project (4 Credits)

The Artificial Intelligence Design Project serves as the culminating experience for students who have completed COMP 3490 and COMP 3491. This course provides an opportunity for students to apply their AI knowledge and skills to a self-directed project in a domain of their choice. Over ten weeks, students will identify a problem, design an AI-driven solution, and develop a functional prototype or proof-of-concept. Emphasizing creativity, problem-solving, and ethical considerations, this capstone-style course enables students to make a tangible impact in their field. The course includes milestone check-ins, peer reviews, and a final presentation to showcase their work.

COMP 3501 Introduction to Artificial Intelligence (4 Credits)

Introduces a variety of Artificial Intelligence concepts and techniques, relevant to a broad range of applications. Students survey multiple techniques including search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language processing. Examines concepts of constraint programming, evolutionary computation and non-standard computation. Prerequisites: COMP 2370.

COMP 3525 Human Centered AI - Mind Reading Machines (4 Credits)

This course explores a key intersection of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). How can AI enhance our interactions with computers by catering to our individual needs and differences? Can we teach computers to understand people’s thoughts and feelings to improve overall interaction? Humans often adjust their communication when they notice the person they’re talking to is frustrated—why can’t computers do the same? In this interdisciplinary, research-focused course, you will read, present, and discuss seminal papers at the intersection of AI and HCI, including (but not limited to) affective computing, physiological computing, augmented cognition, and multimodal interaction. ## Learning Objectives (General Goal) Demonstrate a broad understanding of the intersections between Human-Computer Interaction and Artificial Intelligence, with an emphasis on user modeling. (LO1) Recognize how theories from fields outside of Computer Science can inform and enhance Computer Science practices. (LO2) Develop knowledge of state-of-the-art user modeling techniques, including practical applications and associated challenges. (LO3) Develop critical thinking and scientific communication skills related to current AI topics, including science writing and scientific discourse.

COMP 3531 Deep Learning: Model Design and Application (4 Credits)

This course addresses the foundational concepts and components of Artificial Neural Networks (ANN), highlighting their capabilities, strengths, and weaknesses as a machine learning algorithm. Students taking this course will develop ANN models from scratch in Python as a basis for understanding their design as well as the underlying mechanics and calculations that shape their behavior. Key topics such as forward-backward propagation, loss function characteristics and optimization will be considered in relation to model design and computational efficiency as well as to problems such as exploding and vanishing gradients. Training strategies (e.g., dropout, initialization, batch normalization) will further enable students to assess trade-offs in model bias & variance. Coupled with hands-on assignments, these building blocks provide the knowledge and skills required to effectively design and implement ANN models that are ethically and technically sound. As well as foreground important architectures such as Convolutional ANNs, Recurrent ANNs, LSTMS, and Transformers as well as their applicability to modern problems. Student learning and proficiency will be assessed based on a combination of quizzes, coding assignments, exams, and a culminating project. Prerequisite: COMP 3432.

COMP 3581 Algorithms for Data Science & AI (4 Credits)

This course introduces the design and analysis of algorithms within the context of data science and artificial intelligence. Topics include; asymptotic complexity and algorithm design techniques such as incremental, divide and conquer, dynamic programming, randomization, greedy algorithms, and advanced sorting techniques. Examples to illustrate techniques are drawn from multi-dimensional clustering (k-means and probabilistic), regression, decision trees, order statistics, data mining using apriori algorithms, and algorithms for generating combinatorial objects. Prerequisite: COMP 2370.

COMP 3591 Computational Geometry (4 Credits)

This class deals with the design and implementation of efficient algorithms for problems defined over geometric objects, such as points, lines, polygons, surfaces, etc. The methods and algorithms covered find applications in many areas, including computer graphics (e.g., hidden surface removal), computer-aided design and manufacturing (e.g., 3D printing), machine learning (e.g., supervised and unsupervised classification), geographic information systems (e.g. terrain visibility), robotics (e.g., motion planning), data mining (e.g., dimensionality reduction), and computer vision (3D reconstruction), to name a few. Fundamental geometric problems such as partitioning, proximity, intersection, convexity, visibility, point location, and motion planning are focused on. Efficient data structures and algorithms for their solutions and design techniques germane to the field, such as divide-and-conquer, plane sweep, randomization, duality, etc. are discussed in detail. Practical methods for the robust implementation of geometric algorithms are also covered. Prerequisites: COMP 2300 and COMP 2370.

COMP 3621 Computer Networking (4 Credits)

An introduction to computer networks with an emphasis on Internet protocols. Topics include: internet design, application layer protocols such as SMTP and HTTP, session layer protocols including TCP and UDP, the internet protocol (IP), link layer technology such as Ethernet, and security issues related to networking. Programming experience of client/server architectures using sockets and TCP/UDP through projects is emphasized. Prerequisites: for undergraduates: (COMP 2355 or COMP 2361) and COMP 2370; for graduates COMP 3004 and COMP 3006. Cross listed with COMP 4621.

COMP 3681 Networking for Games (4 Credits)

Implementing the networking code for multiplayer games is a complex task that requires an understanding of performance, security, game design, and advanced programming concepts. In this course, students are introduced to the networking stack and how this is connected to the Internet, learn how to write protocols for games, and implement several large games using a game engine that demonstrate the kind of networking and protocols required by different genres of games. In addition, tools are introduced that help understand and debug networking code, simplify the creation of protocols, and make the development of networking code easier.

COMP 3701 Topics in Computer Graphics (4 Credits)

COMP 3702 Topics in Database (4 Credits)

COMP 3703 Topics-Artificial Intelligence (4 Credits)

COMP 3704 Advanced Topics: Systems (4 Credits)

COMP 3705 Topics in Computer Science (1-4 Credits)

COMP 3711 Special Topics in Data Science & AI (4 Credits)

Special Topics in Data Science & AI highlights selected methods & applications in machine learning that are not specifically addressed within the curriculum. Areas of study may include, but are not limited to, natural language processing, recommender systems, data science for social justice, machine learning operations, generative AI and more. Prerequisites: COMP 3432 and COMP 3441.

COMP 3721 Computer Security (4 Credits)

This course gives students an overview of computer security along with some cryptography. Some network security concepts are also included. Other concepts include coverage of risks and vulnerabilities, policy formation, controls and protection methods, role-based access controls, database security, authentication technologies, host-based and network-based security issues. Prerequisite: COMP 2362 or COMP 3361. Cross listed with COMP 4721.

COMP 3722 Network Security (4 Credits)

Network Security covers tools and techniques employed to protect data during transmission. It spans a broad range of topics including authentication systems, cryptography, key distribution, firewalls, secure protocols and standards, and overlaps with system security concepts as well. This course will provide an introduction to these topics, and supplement them with hands-on experience. Prerequisites: COMP 3721 or permission of instructor.

COMP 3723 Ethical Hacking (4 Credits)

Ethical hacking is the process of probing computer systems for vulnerabilities and exposing their presence through proof-of-concept attacks. The results of such probes are then utilized in making the system more secure. This course will cover the basics of vulnerability research, foot printing targets, discovering systems and configurations on a network, sniffing protocols, firewall hacking, password attacks, privilege escalation, rootkits, social engineering attacks, web attacks, and wireless attacks, among others. Prerequisites: COMP 1203 or COMP 2673 (CS Intro sequence).

COMP 3731 Computer Forensics (4 Credits)

Computer Forensics involves the examination of information contained in digital media with the aim of recovering and analyzing latent evidence. This course will provide students an understanding of the basic concepts in preservation, identification, extraction and validation of forensic evidence in a computer system. The course covers many systems level concepts such as disk partitions, file systems, system artifacts in multiple operating systems, file formats, email transfers, and network layers, among others. Students work extensively on raw images of memory and disks, and in the process, build components commonly seen as features of commercial forensics tools (e.g. file system carver, memory analyzer, file carver, and steganalysis). Prerequisites: COMP 3361; COMP 2355 or 2361 for undergraduates; COMP 3006 for graduates.

COMP 3801 Introduction Computer Graphics (4 Credits)

Fundamentals of 3D rendering including the mathematics behind coordinate systems, projections, clipping, hidden surface removal, shadows, lighting models, shading models, and mapping techniques. Significant use of 3D APIs through shader programming is covered along with the basics of 3D model representation and animations. Satisfies "Advanced Programming" requirements for graduate students. Prerequisites: COMP 2370, MATH 1952 or 1962.

COMP 3820 Introduction to Game Programming (4 Credits)

Learn the fundamentals of game programming by creating 2D and 3D games using a modern game engine. Topics include working with 2D/3D art assets, character controllers, physics, camera systems, 2D and 3D animation integration, lighting, audio, user interfaces, artificial intelligence, and level design. The emphasis in this course is on game programming and implementing game mechanics in fully-working games. Prerequisites: COMP 2381 and COMP 2821.

COMP 3821 Game Programming I (4 Credits)

A continuation of Introduction to Game Programming, this course introduces advanced topics that are essential as future game programmers. Students have the opportunity to learn game engine architecture, 2D and 3D linear algebra for graphics, sprites, 2D and 3D animations, input handling, finite state machines, particle systems, user interfaces, game audio, and artificial intelligence for games. Prerequisites: COMP 2370 and COMP 3820.

COMP 3822 Game Programming II (4 Credits)

In this course, students learn how to work with a 3D game engine and build 3D games. Topics include algorithms, mathematics for 3D game engines, scene management, animations, 3D shaders, particle systems, physics for games, UIs, terrain systems, and working with higher-level scripting languages on top of the low-level implementation language. Prerequisites: COMP 3821. Suggested corequisite or prerequisite: COMP 3801.

COMP 3831 Game Prototyping (4 Credits)

Introduces game prototyping, where game concepts are created and developed into working prototypes using student-chosen game development tools. Engages in critical awareness of game creation practices through discussions, critiques, demos, and player testing. Students have the opportunity to explore their creativity, to expand their knowledge of game design, and to build a broad portfolio of ideas and working projects that demonstrate both their design and technical skillsets. Required for Game Development majors, though all majors are welcome. Prerequisites: COMP 2821.

COMP 3832 Game Capstone I (4 Credits)

Students design, build, critique, and playtest their game prototypes from Game Prototyping. Both art and programming are developed by the student teams with the instructor acting as a project manager to ensure that goals are met through the 10-week development process through various milestones. In addition to building the game, students alter their game design document to add new features, making corrections to prior design issues, and focus on making the game playable and "fun." Prerequisite: COMP 3821 and COMP 3831.

COMP 3833 Game Capstone II (4 Credits)

Students design, build, critique and playtest their working game from Game Capstone I. Both art and programming are developed by the student teams with the instructor acting as a project manager to ensure that goals are met through the 10-week development process through various milestones. In addition to building the game, students modify their design document and implement changes in their game, create new concept art for the features, build an introduction level into their game, test the game with "Play testers", and focus on creating a game that is "fun" to play. By the end of the quarter, their game is ready for distribution on an appropriate platform. Prerequisite: COMP 3832.

COMP 3904 Internship/Co-Op in Computing (0-10 Credits)

Experiential learning through employment with a company to work in computer science. Students are expected to find the internship/co-op and connect their hiring manager with an advisor in the department to get approval. Requirements for approval include a job description that shows what will be accomplished throughout the quarter and a final report from the hiring manager to report on their performance so that a grade may be assigned. Prerequisites: COMP 2370 and approval of department. MS Data Science students: practical experience working under the supervision of data science employer and a data science faculty advisor. Learning outcomes will be assessed via student work such as summary reports, literature reviews, presentations, code notebooks and/or repositories. Prerequisites: permission of Data Science Faculty Director.

COMP 3991 Independent Study (1-10 Credits)

Cannot be arranged for any course that appears in the regular course schedule for that particular year.

COMP 3995 Independent Research (1-10 Credits)