Computer Science & Engineering (CSE)

Courses

CSE 003 Introduction to Programming, Part A 2 Credits

Introduction to programming fundamentals & problem-solving using the Java language. Covers the first half of CSE 007 concepts, including data types, control flow, introduction to methods, arrays and a breadth of computing. No prior programming experience is needed. Cannot be taken by students who have completed CSE 007.
Attribute/Distribution: Q

CSE 004 Introduction to Programming, Part B 2 Credits

Introduction to problem-solving and object-oriented programming (OOP) using the Java language. Covers the second half of CSE 007 concepts, including methods, arrays (including searching & sorting), basics of OOP including data encapsulation, inheritance and polymorphism and a breadth of computing. Cannot be taken by students who have completed CSE 007.
Prerequisites: CSE 003
Attribute/Distribution: Q

CSE 007 Introduction to Programming 0,4 Credits

Introduction to problem-solving and object-oriented programming (OOP) using the Java language. Covers data types, control flow, methods, arrays (including searching & sorting), basics of OOP including data encapsulation, inheritance and polymorphism and a breadth of computing. If credit is given for CSE 007 then no credit will be given for CSE 003 nor CSE 004.
Attribute/Distribution: Q

CSE 012 Introduction to Programming with Python 3 Credits

Fundamental concepts of computing and "computational thinking": problem analysis, abstraction, algorithms, digital representation of information, and networks. Concepts of software development using the Python language. This course will not be considered as a CSE technical elective for CS majors.
Attribute/Distribution: Q

CSE 017 Programming and Data Structures 0,3 Credits

Design and implementation of algorithms and data structures using Java. Assumes that students have prior experience using conditional statements, loops, arrays, and object-oriented programming in Java. Algorithmic techniques such as recursion, algorithm analysis, and sorting. Design and implementation of data structures such as lists, queues, stacks, trees, and hash tables.
Prerequisites: CSE 004 or CSE 007
Attribute/Distribution: Q

CSE 042 (EMC 042) Game Design 3 Credits

Modern topics in game design: Finite State Machines, iterative design process, systems and interactivity, designing rules for digital games, emergence in games, games as Schemas of Uncertainty, games as Information Theory Schemas, games as Information Systems, games as Cybernetic Systems. The course does not count as a technical elective for majors in Computer Science, Computer Science and Business, or Computer Engineering.

CSE 100 Teaching Assistant Workshop 0-1 Credits

This course is required of all teaching assistants and graders. The course covers the roles and responsibilities of teaching assistants and graders and how to create a positive learning environment, effectively communicate, manage the classroom, grade and assess, employ various teaching techniques, and leverage technology in the classroom. The challenges faced by students and their perspective while learning and applying new concepts are discussed throughout. Department approval is required.

CSE 109 Systems Software 0,4 Credits

Design and implementation of modular programs interacting with the operating system through system calls and programming interfaces using the C programming language. Topics covered include data representation and storage, data and bit manipulation, memory management, stages of compilation, file I/O, interprocess communication, network programming, programmatic testing, interactive debugging, and error handling. Good programming practices, including security, and practical methods for implementing medium-scale programs are also emphasized.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 127 (COGS 127) Survey of Artificial Intelligence 3 Credits

An introduction to artificial intelligence (AI) intended for non-majors. AI concepts, systems, and history. Credit will not be given for both CSE/COGS 127 and CSE/COGS 327.
Prerequisites: CSE 004 or CSE 007 or CSE 012
Attribute/Distribution: Q

CSE 140 Foundations of Discrete Structures and Algorithms 0,3 Credits

Basic representations used in algorithms: propositional and predicate logic, set operations and functions, relations and their representations, matrices and their representations, graphs and their representations, trees and their representations. Basic formalizations for proving algorithm correctness: logical consequences, induction, structural induction. Basic formalizations for algorithm analysis: counting, pigeonhole principle, permutations. Credit will not be given for both CSE 140 and MATH 261.
Prerequisites: (MATH 021 or MATH 031 or MATH 051 or MATH 076) and CSE 017
Can be taken Concurrently: CSE 017
Attribute/Distribution: Q

CSE 160 Introduction to Data Science 0,3 Credits

Data Science is a fast-growing interdisciplinary field, focusing on the computational analysis of data to extract knowledge and insight. Collection, preparation, analysis, modeling, and visualization of data, covering both conceptual and practical issues. Examples from diverse fields and hands-on use of statistical and data manipulation software.
Prerequisites: CSE 004 or CSE 007 or CSE 012 or BIS 335
Attribute/Distribution: Q

CSE 190 Special Topics 1-3 Credits

Supervised reading and research. Consent of department required.

CSE 202 Computer Organization and Architecture 3 Credits

Interaction between low-level computer architectural properties and high-level program behaviors: instruction set design; digital logic and assembly language; processor organization; the memory hierarchy; multicore and GPU architectures; and processor interrupt/exception models. Credit will not be given for both CSE 201 and CSE 202.
Prerequisites: CSE 017 or CSE 018

CSE 216 Software Engineering 0,3 Credits

The software lifecycle; lifecycle models; software planning; testing; specification methods; maintenance. Emphasis on team work and large-scale software systems, including oral presentations and written reports.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 217 Computer Science Projects 3 Credits

Project-based learning through independent or small-group projects related to computer systems and/or applications. Students will progress through the software development lifecycle, including high-level design, functional and non-functional requirements, implementation, testing, and maintenance. One large group meeting per week, where students serve as consultants to each other as they present their progress.
Prerequisites: CSE 216
Attribute/Distribution: W

CSE 241 Database Systems and Applications 0,3 Credits

Design of large databases: Integration of databases and applications using SQL and JDBC; transaction processing; performance tuning; data mining and data warehouses. Not available to students who have credit for CSE 341 or ISE 224.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 242 Blockchain Algorithms and Systems 3 Credits

Blockchain system concepts, data structures, and algorithms. Cryptographic algorithms for blockchain security. Distributed consensus algorithms for decentralized control in both a public and permissioned blockchain setting. Smart contracts. Cross-chain transactions. Blockchain databases and enterprise blockchains.
Prerequisites: CSE 109 or CSE 241 or CSE 341
Can be taken Concurrently: CSE 109
Attribute/Distribution: Q

CSE 252 (EMC 252) Computing Ethics 3 Credits

An interactive exploration that provides students with concepts and frameworks to reason about ethical and social issues related with computing. Topics may include: privacy, corporate responsibility, the changing nature of work, language technologies, professional ethics, autonomous systems, online political communication, fairness and bias, environmental impacts, legal regulation, political economy, and other relevant technologies, concepts, issues.
Attribute/Distribution: SW, W

CSE 260 Foundations of Robotics 3 Credits

This course introduces students to the field of robotics, covering foundational mathematics and physics as well as important algorithms and tools. Topics include simulation, kinematics, control, machine learning, and probabilistic inference. The mathematical basis of each area will be covered, followed by practical application to common robotics tasks. This course is designed to be taught remotely using simulated robot platforms and sensors.
Prerequisites: CSE 140
Attribute/Distribution: Q

CSE 261 (MATH 261) Discrete Structures 3 Credits

Topics in discrete structures chosen for their applicability to computer science and engineering. Sets, propositions, induction, recursion; combinatorics; binary relations and functions; ordering, lattices and Boolean algebra; graphs and trees; groups and homomorphisms. Various applications.
Prerequisites: (MATH 021 or MATH 031 or MATH 051 or MATH 076)
Attribute/Distribution: MA

CSE 262 Programming Languages 0,3 Credits

Use, structure and implementation of several programming languages.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 264 Web Systems Programming 3 Credits

Practical experience in designing and implementing modern Web applications. Concepts, tools, and techniques, including: HTTP, HTML, CSS, DOM, JavaScript, Ajax, PHP, graphic design principles, mobile web development. Not available to students who have credit for IE 275.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 265 System and Network Administration 0,3 Credits

Overview of systems and network administration in a networked UNIX-like environment. System installation, configuration, administration, and maintenance; security principles; ethics; network, host, and user management; standard services such as electronic mail, DNS, and WWW; file systems; backups and disaster recovery planning; troubleshooting and support services; automation, scripting; infrastructure planning.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 271 Programming in Linux and Windows Operating Systems 3 Credits

Students learn Linux and Windows operating system fundamentals, including features, history, organization, process management, and file systems. Tools commonly available with these operating systems, such as those for program development, text processing, scheduling jobs, and communications, are also explored. Emphasis is placed on learning the BASh and PowerShell scripting languages, and students should expect to work on a variety of small programming assignments.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 280 Capstone Project I 3 Credits

First of a two semester capstone course sequence that involves the design, implementation, and evaluation of a computer science software project. Conducted by small student teams working from project definition to final documentation. Each student team has a CSE faculty member serving as its advisor. The first semester emphasis is on project definition, planning and implementation. Communication skills such as technical writing, oral presentations, and use of visual aids are also emphasized. Project work is supplemented by weekly seminars.
Prerequisites: CSE 216
Can be taken Concurrently: CSE 216
Attribute/Distribution: W

CSE 281 Capstone Project II 0,3 Credits

Second of a two semester capstone course sequence that involves the design, implementation, and evaluation of a computer science software project; conducted by small student teams working from project definition to final documentation; each student team has a CSE faculty member serving as its advisor; The second semester emphasis is on project implementation, verification & validation, and documentation requirements. It culminates in a public presentation and live demonstration to external judges as well as CSE faculty and students.
Prerequisites: CSE 280
Attribute/Distribution: W

CSE 300 Apprentice Teaching 1-4 Credits

Practical teaching experience under supervision of an experienced instructor. Students learn fundamentals of teaching, including course and lecture planning, instructional delivery, classroom environment and management, and assessment. Students will benefit from significant hands-on experience in the lectures, recitations, and office hours. Department approval is required.
Repeat Status: Course may be repeated.

CSE 302 Compiler Design 3 Credits

Principles of artificial language description and design. Sentence parsing techniques, including operator precedence, bounded-context, and syntax-directed recognizer schemes. The semantic problem as it relates to interpreters and compilers. Dynamic storage allocation, table grammars, code optimization, compiler-writing languages.
Prerequisites: (CSE 109)
Attribute/Distribution: Q

CSE 303 Operating System Design 3 Credits

Process and thread programming models, management, and scheduling. Resource sharing and deadlocks. Memory management, including virtual memory and page replacement strategies. I/O issues in the operating system. File system implementation. Multiprocessing. Computer security as it impacts the operating system.
Prerequisites: ECE 201 or (CSE 201 or CSE 202) and CSE 109
Attribute/Distribution: Q

CSE 307 (BIOE 307) Structural Bioinformatics 3 Credits

Computational techniques and principles of structural biology used to examine molecular structure, function, and evolution. Topics include: protein structure alignment and prediction; molecular surface analysis; statistical modeling; QSAR; computational drug design; influences on binding specificity; protein-ligand, -protein, and -DNA interactions; molecular simulation, electrostatics. Tutorials on UNIX systems and research software support an interdisciplinary collaborative project in computational structural biology. Credit will not be given for both CSE 307 and CSE 407. Must have junior standing or higher.
Prerequisites: BIOS 120 or CSE 109 or CHM 113 or MATH 231
Attribute/Distribution: Q

CSE 308 (BIOE 308) Genomics 3 Credits

Biological discoveries and computational techniques affecting modern biotechnology. Genome assembly, genome annotation, and genome evolution in three interdisciplinary projects. No prior background in biology is assumed. Credit will not be given for both CSE 308 (BIOE 308) and CSE 408 (BIOE 408).
Prerequisites: CSE 017

CSE 310 (BSTA 310) Assistive Technologies 3 Credits

This class will introduce typical challenges faced by persons with disabilities and the role of assistive technologies (ATs) in solving such challenges. The class will examine opportunities presented by recent advances in mobile and AI technologies. Working in groups, each student will be expected to acquire and apply relevant skills in designing AT solutions. The class can be taken by students with diverse backgrounds including the following: community and population health, social and behavioral sciences, business, engineering and computer science.
Prerequisites: CSE 017 or (BSTA 101 and BSTA 102)

CSE 313 Computer Graphics 3 Credits

Algorithms and programming techniques for generating three dimensional computer graphics. Rasterization, color, animation, interaction, textures, lighting models, ray tracing. Substantial focus on the interaction between the CPU and the GPU, relating to vertex and fragment shaders.
Prerequisites: CSE 109 and (MATH 043 or MATH 205 or MATH 242)
Attribute/Distribution: Q

CSE 318 Introduction to the Theory of Computation 3 Credits

Provides a deep understanding of computation, its capabilities and its limitations. The course uses discrete formal methods to (1) formulate precise definitions of three kinds of finite-state machines (finite automata, pushdown automata, and Turing machines); (2) prove properties of these machines by studying their expressiveness (i.e., the kinds of problems that can be solved with these machines), and (3) study computational problems that cannot be solved with algorithms.
Prerequisites: CSE 140
Attribute/Distribution: Q

CSE 319 Image Analysis and Graphics 3 Credits

State-of-the-art techniques for fundamental image analysis tasks: feature extraction, segmentation, registration, tracking, recognition, search (indexing and retrieval). Related computer graphics techniques: modeling (geometry, physically-based, statistical), simulation (data-driven, interactive), animation, 3D image visualization, and rendering. Credit will not be given for both CSE 319 and CSE 419.
Prerequisites: CSE 313
Attribute/Distribution: Q

CSE 320 (BIOE 320) Biomedical Image Computing and Modeling 3 Credits

Biomedical image modalities, image computing techniques, and imaging informatics systems. Understanding, using, and developing algorithms and software to analyze biomedical image data and extract useful quantitative information: Biomedical image modalities and formats; image processing and analysis; geometric and statistical modeling; image informatics systems in biomedicine. Credit will not be given for both CSE 320 and CSE 420.
Prerequisites: (MATH 205 or MATH 043) and CSE 017
Attribute/Distribution: Q

CSE 323 Computer Vision 3 Credits

Fundamental techniques from image processing, pattern recognition, machine learning and deep learning used to process and understand visual data. Build full pipelines for solutions to classic vision problems such as object detection and recognition, image matching and retrieval, and scene understanding and reconstruction. New and challenging problems such as synthetic image generation. Credit will not be given for both CSE 323 and CSE 423.
Prerequisites: ((MATH 205 or MATH 242) and (MATH 231 or ECO 045) and CSE 017) or (DSCI 311 and DSCI 321)

CSE 325 Natural Language Processing 3 Credits

Overview of modern natural language processing techniques: text normalization, language model, part-of-speech tagging, hidden Markov model, syntactic and dependency parsing, semantics, word sense, reference resolution, dialog agent, machine translation. Design, implementation and evaluation of classic NLP algorithms. Credit will not be given for both CSE 325 and CSE 425.
Prerequisites: (MATH 231 or ECO 045 or ISE 121) and CSE 017 and (MATH 205 or MATH 241 or MATH 242) and (CSE 160 or CSE 326 or CSE 327 or MATH 365 or ECE 414 or ISE 364 or ISE 365 or ISE 367)
Attribute/Distribution: Q

CSE 326 Fundamentals of Machine Learning 3 Credits

Bayesian decision theory and the design of parametric and nonparametric classification and regression: linear, quadratic, nearest-neighbors, neural nets. Boosting, bagging. Credit will not be given for both CSE 326 and CSE 426.
Prerequisites: CSE 017 and (MATH 205 or MATH 043) and (MATH 231 or ISE 121 or ECO 045)

CSE 327 (COGS 327) Artificial Intelligence Theory and Practice 3 Credits

Detailed analysis of a broad range of artificial intelligence (AI) algorithms and systems. Problem solving, knowledge representation, reasoning, planning, uncertainty and machine learning. Applications of AI to areas such as natural language processing, vision, and robotics. Credit will not be given for both CSE/COGS 127 and CSE/COGS 327.
Prerequisites: CSE 017 and CSE 140
Attribute/Distribution: Q

CSE 331 User Interface Systems and Techniques 3 Credits

Principles and practice of creating effective human-computer interfaces. Design and user evaluation of user interfaces; design and use of interface building tools. Programming projects using a variety of interface building tools to construct and evaluate interfaces.
Prerequisites: CSE 017
Attribute/Distribution: Q

CSE 333 Methods for Understanding Human-Computer Interactions 3 Credits

Covers a variety of methods for conducting research on human interactions with computing systems. Methods covered may include: controlled experiments, surveys, ethnography, grounded theory, research through design, log data analysis, retrospective techniques, social network analysis, and others. Taking this course will familiarize the student with several of these methods through readings and in-class discussion and activities, as well as provide the student the opportunity to gain significant experience applying one of these methods in a project.
Prerequisites: CSE 252 or CSE 331

CSE 335 Topics on Intelligent Decision Support Systems 3 Credits

Intelligent decision support systems (IDSSs). AI techniques that are used to build IDSSs: case-based reasoning, decision trees and knowledge representation. Applications of these techniques: help-desk systems, e-commerce, and knowledge management. Credit will not be given for both CSE 335 and CSE 435.
Prerequisites: CSE 327 or CSE 109
Attribute/Distribution: Q

CSE 336 (ECE 336) Embedded Systems 3 Credits

Use of small computers embedded as part of other machines. Limited-resource microcontrollers and state machines from high description language. Embedded hardware: RAM, ROM, flash, timers, UARTs, PWM, A/D, multiplexing, debouncing. Development and debugging tools running on host computers. Real-Time Operating System (RTOS) semaphores, mailboxes, queues. Task priorities and rate monotonic scheduling. Software architectures for embedded systems.
Prerequisites: CSE 017

CSE 337 Reinforcement Learning 3 Credits

Algorithms for automated learning from interactions with the environment to optimize long-term performance. Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo reinforcement learning methods. Credit will not be given for both CSE 337 and CSE 437.
Prerequisites: (MATH 231, ECO 045) and CSE 109
Attribute/Distribution: Q

CSE 340 (MATH 340) Design and Analysis of Algorithms 0,3 Credits

Algorithms for searching, sorting, manipulating graphs and trees, finding shortest paths and minimum spanning trees, scheduling tasks, etc.: proofs of their correctness and analysis of their asymptotic runtime and memory demands. Designing algorithms: recursion, divide-and-conquer, greediness, dynamic programming. Limits on algorithm efficiency using elementary NP-completeness theory.
Prerequisites: (MATH 021 or MATH 031 or MATH 076) and CSE 140 and CSE 017
Attribute/Distribution: Q

CSE 341 Database Systems, Algorithms, and Applications 3 Credits

Design of large databases; normalization; query languages (including SQL); Transaction-processing protocols; Query optimization; performance tuning; distributed systems. Not available to students who have credit for CSE 241.
Prerequisites: CSE 017 and CSE 140
Attribute/Distribution: Q

CSE 342 Fundamentals of Internetworking 3 Credits

Architecture and protocols of computer networks. Protocol layers; network topology; data-communication principles, including circuit switching, packet switching and error control techniques; sliding window protocols, protocol analysis and verification; routing and flow control; local and wide area networks; network interconnection; client-server interaction; emerging networking trends and technologies; topics in security and privacy.
Prerequisites: CSE 109
Attribute/Distribution: Q

CSE 343 Network Security 3 Credits

Overview of network security threats and vulnerabilities. Techniques and tools for detecting, responding to and recovering from security incidents. Fundamentals of cryptography. Hands-on experience with programming techniques for security protocols. Credit will not be given for both CSE 343 and CSE 443.
Prerequisites: CSE 202 or CSE 271 or CSE 342
Attribute/Distribution: Q

CSE 345 WWW Search Engines 3 Credits

Study of algorithms, architectures, and implementations of WWW search engines; Information retrieval (IR) models; performance evaluation; properties of hypertext crawling, indexing, searching and ranking; link analysis; parallel and distributed IR; user interfaces. Credit will not be given for both CSE 345 and CSE 445.
Prerequisites: CSE 109
Attribute/Distribution: Q

CSE 347 Data Mining 3 Credits

Overview of modern data mining techniques: data cleaning; attribute and subset selection; model construction, evaluation and application. Fundamental mathematics and algorithms for decision trees, covering algorithms, association mining, statistical modeling, linear models, neural networks, instance-based learning and clustering covered. Practical design, implementation, application, and evaluation of data mining techniques in class projects. Credit will not be given for both CSE 347 and CSE 447.
Prerequisites: CSE 017 and (CSE 160 or CSE 326) and (MATH 231 or MATH 205 or MATH 241 or MATH 242 or ECO 045 or ISE 121)
Attribute/Distribution: Q

CSE 348 AI Game Programming 3 Credits

Contemporary computer games: techniques for implementing the program controlling the computer component; using Artificial Intelligence in contemporary computer games to enhance the gaming experience: pathfinding and navigation systems; group movement and tactics; adaptive games, game genres, machine scripting language for game designers, and player modeling. Credit will not be given for both CSE 348 and CSE 448.
Prerequisites: CSE 327 or CSE 109
Attribute/Distribution: Q

CSE 349 Big Data Analytics 3 Credits

Provides working knowledge of large-scale data analysis using open source frameworks such as Apache Spark and Waikato Environment for Knowledge Analysis (Weka). Includes patterns employed in big data analytics, including classification, collaborative filtering, recommender systems, natural language processing, simulation, deep learning, and anomaly detection. Project-oriented software course; students should have substantial programming experience in one or more high-level languages. Past experience in data mining and/or machine learning expected. Credit will not be given for both 349 and 449.
Prerequisites: CSE 109 and (CSE 326 or CSE 347)

CSE 350 Special Topics 3 Credits

Selected topics in the field of computer science not included in other courses.
Repeat Status: Course may be repeated.
Prerequisites: MATH 205

CSE 351 Iterative Methods 3 Credits

Commonly used iterative methods for efficiently solving large, sparse linear systems. Review of basic numeric analysis, direct methods, iterative methods, Krylov subspace methods, preconditioning techniques, and multigrid methods. Analysis of theoretical properties, including convergence behavior and computational and memory demands. Evaluation of performance using numerical experimentation on various real-world applications using Matlab. Credit will not be given for both CSE 351 and CSE 451.
Prerequisites: CSE 109 and CSE 140 and (MATH 205 or MATH 043)

CSE 360 Introduction to Mobile Robotics 3 Credits

Algorithms employed in mobile robotics for navigation, sensing, and estimation. Common sensor systems, motion planning, robust estimation, bayesian estimation techniques, Kalman and Particle filters, localization and mapping. Credit will not be given for both CSE 360 and CSE 460.
Prerequisites: MATH 205
Attribute/Distribution: Q

CSE 367 Blockchain Projects 0,3 Credits

Independent or small-group unique projects related to blockchain systems and/or applications. While pursuing their own project, students serve as consultants to the other teams via a once-weekly class meeting in which each team presents updates on status, progress, and open problems, and one student gives a longer prepared presentation on current research or development results in the blockchain field. Each project team has its own separate second weekly meeting with the instructor for a more in-depth project review and discussion.
Repeat Status: Course may be repeated.
Prerequisites: CSE 242
Attribute/Distribution: Q

CSE 371 Principles of Mobile Computing 3 Credits

Fundamental concepts and technology underlying mobile computing. Current research in these areas. Examples drawn from a variety of application domains such as health monitoring, energy management, commerce, and travel. Issues of system efficiency will be studied, including efficient handling of large data such as images and effective use of cloud storage. Recent research papers will be discussed. Credit will not be given for both CSE371 and CSE471.
Prerequisites: (CSE 109 and (CSE 202 or ECE 201), )
Attribute/Distribution: Q

CSE 375 Principles of Practice of Parallel Computing 3 Credits

Parallel computer architectures, parallel languages, parallelizing compilers and operating systems. Design, implementation, and analysis of parallel algorithms for scientific and data-intensive computing. Credit is not given for both CSE 375 and CSE 475.
Prerequisites: (ECE 201 or CSE 201) or CSE 303 or CSE 202
Can be taken Concurrently: ECE 201, CSE 201, CSE 303, CSE 202
Attribute/Distribution: Q

CSE 376 Distributed Systems 3 Credits

Exploration of theoretical and practical aspects of topics in distributed systems through a combination of readings, programming assignments, and projects. The main focal point is large distributed systems, in particular protocols to synchronize the activities of machines when operating over shared data. Techniques to ensure fault-tolerance and service-availability will also be discussed. Using distributed systems as a foundation, students gain skills in the design of complex, multilayered systems. Credit will not be given for both CSE 376 and CSE 476.
Prerequisites: CSE 303 and CSE 340 and (CSE 241 or CSE 242 or CSE 341 or CSE 375)
Attribute/Distribution: Q

CSE 389 Honors Project 1-8 Credits

CSE 392 Independent Study 1-3 Credits

An intensive study, with report, of a topic in computer science which is not treated in other courses. Consent of instructor required.
Repeat Status: Course may be repeated.

CSE 401 (ECE 401) Advanced Computer Architecture 3 Credits

Design, analysis and performance of computer architectures; high-speed memory systems; cache design and analysis; modeling cache performance; principle of pipeline processing, performance of pipelined computers; scheduling and control of a pipeline; classification of parallel architectures; systolic and data flow architectures; multiprocessor performance; multiprocessor interconnections and cache coherence.

CSE 403 Advanced Operating Systems 3 Credits

Principles of operating systems with emphasis on hardware and software requirements and design methodologies for multi-programming systems. Global topics include the related areas of process management, resource management, and file systems.
Prerequisites: CSE 303

CSE 404 (ECE 404) Computer Networks 3 Credits

Study of architecture and protocols of computer networks. The ISO model; network topology; data-communication principles, including circuit switching, packet switching and error control techniques; sliding window protocols, protocol analysis and verification; routing and flow control; local area networks; network interconnection; topics in security and privacy.

CSE 405 Advanced Programming Languages 3 Credits

Basic ideas behind modern programming language design, with a focus on functional languages: type systems, modularity, operational semantics, and others. Students need to have some mathematical maturity, including familiarity with proof techniques such as induction.

CSE 406 Research Methods 3 Credits

Technical writing, reading the literature critically, analyzing and presenting data, conducting research, making effective presentations, and understanding social and ethical responsibilities. Topics drawn from probability and statistics, use of scripting languages, and conducting large-scale experiments. Must have first-year status in either the CS or CompE Ph. D. program.

CSE 407 (BIOE 407) Structural Bioinformatics 3 Credits

Computational techniques and principles of structural biology used to examine molecular structure, function, and evolution. Topics include: protein structure alignment and prediction; molecular surface analysis; statistical modeling; QSAR; computational drug design; influences on binding specificity; protein-ligand, -protein, and –DNA interactions; molecular simulation, electrostatics. This course, a version of 307 for graduate students, requires advanced assignments and a collaborative project. Credit will not be given for both CSE 307 and 407. Consent of instructor required.

CSE 408 (BIOE 408) Bioinformatics: Issues and Algorithms 3 Credits

Computational problems and their associated algorithms arising from the creation, analysis, and management of bioinformatics data. Genetic sequence comparison and alignment, physical mapping, genome sequencing and assembly, clustering of DNA microarray results in gene expression studies, computation of genomic rearrangements and evolutionary trees. This course, a version of 308 for graduate students requires advanced assignments. Credit will not be given for both BIOE 308 (CSE 308) and BIOE 408 (CSE 408). No prior background in biology is assumed.
Prerequisites: CSE 017 or CSE 018

CSE 409 Theory of Computation 3 Credits

Finite automata. Pushdown automata. Relationship to definition and parsing of formal grammars. Credits will not be given for both CSE318 and CSE409.
Prerequisites: CSE 318 or CSC 318

CSE 410 (BSTA 410) Assistive Technologies 3 Credits

CSE 411 Advanced Programming Techniques 3 Credits

Deeper study of programming and software engineering techniques. The majority of assignments involve programming in contemporary programming languages. Topics include memory management, GUI design, testing, refactoring, and writing secure code.

CSE 413 Computer Graphics 3 Credits

Computer graphics for animation, visualization, and production of special effects: displays, methods of interaction, images, image processing, color, transformations, modeling (primitives, hierarchies, polygon meshes, curves and surfaces, procedural), animation (keyframing, dynamic simulation), rendering and realism (shading, texturing, shadows, visibility, ray tracing), and programmable graphics hardware. Department approval required. Credit will not be given for both CSE 313 and CSE 413.

CSE 418 Theory of Computation 3 Credits

Finite automata. Pushdown automata. Relationship to definition and parsing of formal grammars. Credit may be given for only one of the following: CSE318 and CSE409 and CSE418.

CSE 419 Image Analysis and Graphics 3 Credits

State-of-the-art techniques for fundamental image analysis tasks; feature extraction, segmentation, registration, tracking, recognition, search (indexing and retrieval). Related computer graphics techniques: modeling (geometry, physically-based, statistical), simulation (data-driven, interactive), animation, 3D image visualization, and rendering. This course, a graduate version of CSE 319, requires additional advanced assignments. Credit will not be given for both CSE 319 and CSE 419.

CSE 420 (BIOE 420) Biomedical Image Computing and Modeling 3 Credits

Biomedical image modalities, image computing techniques, and imaging informatics systems. Understanding, using, and developing algorithms and software to analyze biomedical image data and extract useful quantitative information: Biomedical image modalities and formats; image processing and analysis; geometric and statistical modeling; image informatics systems in biomedicine. This course, a graduate version of BIOE 320, requires additional advanced assignments. Credit will not be given for both BIOE 320 and BIOE 420.
Prerequisites: MATH 205 and CSE 109
Attribute/Distribution: ND

CSE 424 Advanced Communication Networks 3 Credits

Current and emerging research topics in communication networks: network protocols, network measurement, internet routing, network security, adhoc and sensor networks, disruption tolerant networks. Lecture, readings, and discussion, plus a project.
Prerequisites: CSE 342 or CSE 303 or CSE 404

CSE 425 Natural Language Processing 3 Credits

Overview of modern natural language processing techniques: text normal- ization, language model, part-of-speech tagging, hidden Markov model, syntatic and dependency parsing, semantics, word sense, reference resolution, dialog agent, machine translation. Three projects to design, implement and evaluate classic NLP algorithms. Credit will not be given for both CSE 325 and CSE 425.
Prerequisites: (MATH 231 or ECO 045) and CSE 017

CSE 426 Fundamentals of Machine Learning 3 Credits

Bayesian decision theory and the design of parametric and nonparametric classification and regression: linear, quadratic, nearest-neighbors, neural nets. Boosting, bagging. This course, a version of CSE 326 for graduate students requires advanced assignments. Credit will not be given for both CSE 326 and CSE 426.

CSE 428 Semantic Web Topics 3 Credits

Theory, architecture and applications of the Semantic Web. Issues in designing distributed knowledge representation languages, ontology development, knowledge acquisition, scalable reasoning, integrating heterogeneous data sources, and web-based agents.

CSE 431 Intelligent Agents 3 Credits

Principles of rational autonomous software systems. Agent theory; agent architectures, including logic-based, utility-based, practical reasoning, and reactive; multi-agent systems; communication languages; coordination methods including negotiation and distributed problem solving; applications.

CSE 433 Advanced Methods for Understanding Human-Computer Interactions 3 Credits

CSE 435 Topics on Intelligent Decision Support Systems 3 Credits

AI techniques used to build IDSSs: case-based reasoning, decision trees and knowledge representation. Applications: helpdesk systems, e-commerce, and knowledge management. This course, a version of CSE 335 for graduate students, requires research projects and advanced assignments. Credit will not be given for both CSE 335 and CSE 435.

CSE 437 Reinforcement Learning and Markov Decision Precesses 3 Credits

Formal model based on Markov decision processes for automated learning from interactions with stochastic, incompletely known environments. Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo reinforcement learning methods. Credit will not be given for both CSE 337 and CSE 437. Must have graduate standing in Computer Science or have consent of instructor.

CSE 440 Advanced Algorithms 3 Credits

Average-case runtime analysis of algorithms. Randomized algorithms and probabilistic analysis of their performance. Analysis of data structures including hash tables, augmented data structures with order statistics. Amortized analysis. Elementary computational geometry. Limits on algorithm space efficiency using PSPACE-completeness theory. Credit will not be given for both CSE 440 and CSE 441.
Prerequisites: CSE 340 or MATH 340

CSE 442 Advanced Blockchain Systems and Theory 3 Credits

Formal foundations of blockchain systems: cryptography, consensus, zero-knowledge proofs, transaction processing both on-chain and cross-chain, validation, and governance. Algorithms and data structures for blockchain systems. Programming paradigms for smart contracts. Current research in blockchain drawing from the cryptography, database, operating system, and parallel computing research communities.
Prerequisites: CSE 241 or CSE 341 or CSE 303 or CSE 403 or CSE 375 or CSE 475

CSE 443 Network Security 3 Credits

Overview of network security threats and vulnerabilities. Techniques and tools for detecting, responding to and recovering from security incidents. Fundamentals of cryptography. Hands-on experience with programming techniques for security protocols. This course, a version of CSE 343 for graduate students, requires research projects and advanced assignments. Credit will not be given for both CSE 343 and CSE 443.
Prerequisites: (CSE 404 or ECE 404) or CSE 271 or CSE 202 or CSE 342

CSE 445 WWW Search Engines 3 Credits

Study of algorithms, architectures, and implementations of WWW search engines. Information retrieval (IR) models; performance evaluation; properties of hypertext crawling, indexing, searching and ranking; link analysis; parallel and distributed IR; user interfaces. This course, a version of CSE 345 for graduate students, requires research projects and advanced assignments. Credit will not be given for both CSE 345 and CSE 445.

CSE 447 Data Mining 3 Credits

Modern data mining techniques: data cleaning; attribute and subset selection; model construction, evaluation and application. Algorithms for decision trees, covering algorithms, association rule mining, statistical modeling, model and regression trees, neural networks, instance-based learning and clustering covered. This course, a version of CSE 347 for graduate students, requires research projects and advanced assignments, and expects students to have a background in probability, statistics, and programming. Credit will not be given for both CSE 347 and CSE 447.
Prerequisites: CSE 326

CSE 449 Big Data Analytics 3 Credits

CSE 450 Special Topics 3 Credits

Selected topics in computer science not included in other courses.
Repeat Status: Course may be repeated.

CSE 460 Mobile Robotics 3 Credits

Algorithms employed in mobile robotics for navigation, sensing, and estimation. Common sensor systems, motion planning, robust estimation, Bayesian estimation techniques, Kalman and particle filters, localization and mapping. This course, a version of CSE 360 for graduate students will require an independent project to be presented in class. Credit will not be given for both CSE 360 and CSE 460.

CSE 467 Blockchain Projects 0,3 Credits

Independent or small-group graduate-level unique projects related to blockchain-systems and/or applications. While pursuing their own project, students serve as consultants to the other teams via a once-weekly class meeting in which each team presents updates on status, progress, and open problems, and one student gives a longer prepared presentation on current research or development results in the blockchain field. Each project team has its own separate second weekly meeting with the instructor for a more in-depth project review and discussion.
Repeat Status: Course may be repeated.

CSE 471 Principles of Mobile Computing 3 Credits

Course topics include fundamental concepts and technology underlying mobile computing and current research in these areas. Examples drawn from a variety of application domains such as health monitoring, energy management, commerce, and travel. Issues of system efficiency will be studied, including efficient handling of large data such as images and effective use of cloud storage. Recent research papers will be discussed. The graduate version of CSE 371 requires additional effort. Credit will not be given for both CSE371 and CSE471.
Prerequisites: CSE 109 and CSE 202 or CSE 303

CSE 475 Principles and Practice of Parallel Computing 3 Credits

Parallel computer architectures, parallel languages, parallelizing compilers and operating systems. Design, implementation, and analysis of parallel algorithms for scientific and data-intensive computing. This is a graduate version of CSE 375. As such, it will require additional assignments. Credit is not given for both CSE 375 and CSE 475.

CSE 476 Distributed Systems 3 Credits

Exploration of theoretical and practical aspects of topics in distributed systems through a combination of readings, programming assignments, and projects. The main focal point is large distributed systems, in particular protocols to synchronize the activities of machines when operating over shared data. Techniques to ensure fault-tolerance and service-availability will also be discussed. Using distributed systems as a foundation, students gain skills in the design of complex, multilayered systems. Credit will not be given for both CSE 376 and CSE 476.
Prerequisites: (CSE 303 or CSE 403) and (CSE 340 or CSE 440) and (CSE 241 or CSE 242 or CSE 341 or CSE 375 or CSE 404 or ECE 404 or CSE 475)

CSE 490 Thesis 1-6 Credits

Thesis.
Repeat Status: Course may be repeated.

CSE 491 Research Seminar 1-3 Credits

Regular meetings focused on specific topics related to the research interests of department faculty. Current research will be discussed. Students may be required to present and review relevant publications. Consent of instructor required.
Repeat Status: Course may be repeated.

CSE 492 Independent Study 1-3 Credits

An intensive study, with report of a topic in computer science that is not treated in other courses. Consent of instructor required.
Repeat Status: Course may be repeated.

CSE 499 Dissertation 1-15 Credits