Computer Science (CS)

Vertically-related courses in this subject field are: 

CS 112 Computational Thinking and Problem Solving

3 credits
Gen Ed: Mathematics
CS 112 carries no credit after CS 120. Introduction to computational thinking and problem solving, including elementary computing concepts such as variables, loops, functions, lists, conditionals, concurrency, data types, simple object oriented concepts, I/O, events, syntax, structured programming, basic concepts of computer organization, editing and the influence of computers in modern society.
Prereq: MATH 108 with a grade of ‘C’ or better; or sufficiently high ACT, SAT, or Math Placement Test score to qualify for MATH 143

CS 120 Computer Science I

4 credits
Fundamental programming constructs, algorithms and problem-solving, fundamental data structures, overview of programming languages, virtual machines, introduction to language translation, declarations and types, abstraction mechanisms, object-oriented programming. This course includes a lab.
Prereq: MATH 143 with a grade of ‘C’ or higher or CS 112 with a grade of ‘C’ or higher; or sufficiently high ACT, SAT, or Math Placement Test score to qualify for MATH 170

CS 121 Computer Science II

3 credits
Abstract data types and data structures: linked lists, stacks, queues, trees, and graphs. Methods to implement and algorithms to manipulate these structures. Dynamic memory methods, sequential file processing, additional searching and sorting algorithms, recursion, and object-oriented programming.
Prereq: CS 120 with a grade of ‘C’ or higher
Coreq: MATH 176

CS 150 Computer Organization and Architecture

3 credits
Digital logic and digital systems, Machine level representation of data, Assembly level machine organization, Memory system organization and architecture, Interfacing and communication, Functional organization, Multiprocessing and alternative architectures.
Prereq: CS 120

CS 204 (s) Special Topics

Credit arranged

CS 210 Programming Languages

3 credits
Major features of good programming languages, with primary emphasis on language features and their role in writing good software; programming language design alternatives; various types of languages, including procedure, data-flow, functional, and object-oriented languages.
Prereq: CS 121

CS 240 Computer Operating Systems

3 credits
Overview of operating systems, Operating system principles, Concurrency, Scheduling and dispatch, Memory management, Introduction to net-centric computing, OS security. Process management. Concurrent programming using threads.
Prereq: CS 121 and CS 150
Coreq: CS 270

CS 270 System Software

3 credits
Programming productivity tools such as make. Debugging tools. Linking and loading. Shell programming and scripting languages. Process management and interprocess communication. Exception handling. Network concepts and network programming.
Prereq: CS 121

CS 298 (s) Internship

Credit arranged

CS 299 (s) Directed Study

Credit arranged

CS 324 Computer Graphics

3 credits
Use of the computer to define, store, manipulate, and display 2D and 3D objects; 2D curvefitting and 3D surface development. Cooperative: open to WSU degree-seeking students.
Prereq: CS 121 and MATH 330

CS 328 Introduction to Computer Game Development

3 credits
An introduction to data structures, algorithms, and programming techniques useful in the development of computer games. Topics including 2D graphics, sound programming, user interfaces, game genres, computerization of classic board games and simulation games.
Prereq: CS 210 and CS 240

CS 336 Introduction to Information Assurance

3 credits
Introduces the confidentiality, availability and integrity goals of information systems; resistance, recognition and response categories of assurance. Focus on computer security and survivability, including cryptography, network security, general purpose operating system security and dependability and special purpose systems for high assurance security and dependability.
Prereq: CS 240

CS 360 Database Systems

3 credits
Study of database design and implementation; comparison of basic models (entity-relationship, hierarchical, network, relational); study of query languages; discussion of issues of integrity, security, dependencies, and normal forms.
Prereq: CS 240 and CS 270

CS 383 Software Engineering

4 credits
Current topics in development of software systems; software life cycle model, requirements definition, requirements analysis, software specification, software architectural design, engineering discipline in software development, software measurement, user interface design, legal and ethical issues in software product development. Projects are developed to demonstrate application of concepts.
Prereq: CS 210, CS 240, and CS 270 or Permission

CS 385 Theory of Computation

3 credits
Cross-listed with MATH 385.
Mathematical models of computation, including finite automata and Turing machines. (Fall only)
Prereq: Permission

CS 395 Analysis of Algorithms

3 credits
Cross-listed with MATH 395.
Measures of efficiency; standard methods and examples in the design, implementation, and analysis of algorithms. (Spring only)
Prereq: MATH 175 and CS 121

CS 398 (s) Computer Science Cooperative Internship

1-3 credits, max 3
Supervised internship in professional computer science settings, integrating academic study with work experience; requires formal plan of activities before co-op assignment and final written report evaluated by on-campus faculty members. Graded P/F.
Prereq: Permission

CS 400 (s) Seminar

Credit arranged
Technical topics, employment practices, interviewing, and current research topics. Graded P/F. One lecture a week.
Prereq: Senior standing in CS

CS 401 Contemporary Issues in Computer Science

1 credit
Ethical, legal, social, and intellectual property issues; current research topics; and other issues of importance to the professional computer scientist. Graded P/F.
Prereq: Senior standing in CS

CS 404 (s) Special Topics

Credit arranged

CS 411 Parallel Programming

3 credits
Joint-listed with CS 511
Analysis, mapping, and the application of parallel programming software to high-performance systems; the principles of spatial- and temporal-locality of data memory hierarchies in performance tuning; architectural considerations in the design and implementation of a parallel program; the tradeoff between threaded (shared memory) and message-passing (distributed memory) programming styles and performance. Additional projects/assignments required for graduate credit. Recommended Preparation: Proficiency in programming using a modern language such as C or C++.
Prereq: CS 395

CS 415 Computational Biology: Sequence Analysis

3 credits
Joint-listed with CS 515
Design and analyze algorithms that address the computational problems posed by biological sequence data, such as DNA or protein sequences. Topics may include: comparing sequences (from genes to genomes), database searching, multiple sequence alignment, phylogenetic inferencing, gene discovery and annotation, and genome assembly. Additional class presentation and/or paper required for graduate credit.
Prereq: Knowledge of high level programming language, basic probability theory, basic molecular biology, or Permission

CS 420 Data Communication Systems

3 credits
Joint-listed with CS 520.
Concept and terminology of data communications, equipment, protocols (including ISO/OSI and TCP/IP), architectures; transmission alternatives, regulatory issues and network management. Additional projects/assignments required for graduate credit.
Prereq: CS 150 and CS 240

CS 428 Multi-User Games and Virtual Environments

3 credits
Joint-listed with CS 528.
Software design and programming issues involved in constructing multi-user computer games and virtual environments, incorporating networking and 3D graphics. Additional projects and assignments required for graduate credit.
Prereq: CS 210, CS 324, and CS 328

CS 431 (s) SFS Professional Development

3 credits
Joint-listed with CS 531.
This course is reserved for CyberCorps(R) Scholarship for Service program participants.
Prereq: Instructor Permission

CS 438 Network Security

3 credits
Joint-listed with CS 538.
Practical topics in network security; policy and mechanism, malicious code; intrusion detection, prevention, response; cryptographic techniques for privacy and integrity; emphasis on trade-offs between risk of misuse, cost of prevention, and societal issues; concepts implemented in programming assignments. Additional projects/assignments required for graduate credit. Recommended Preparation: Knowledge of C or C++. Cooperative: open to WSU degree-seeking students.
Prereq: CS 336

CS 439 Applied Security Concepts

3 credits
Joint-listed with CS 539.
Hands-on approach to computer security with emphasis on developing practical knowledge of how cyber attacks work and how to defend against them. Detailed exploration of attacks such as buffer overruns, string attacks, worms, trojan horses, and denial-of-service attacks, and development of defenses against them. Additional work required for graduate credit. Recommended preparation: Good knowledge of C, operating system concepts and Unix.
Prereq: CS 336 or Permission

CS 441 Advanced Operating Systems

3 credits
Joint-listed with CS 541
Principles of contemporary operating systems for network and distributed computer systems; sequential processes, scheduling, process synchronization, device management, file systems, memory management, and protection and security. Additional work required for graduate credit.
Prereq: CS 240

CS 444 Supervisory Control and Critical Infrastructure Systems

1 credit
Joint-listed with CS 544, Cross-listed with ECE 444
Principles of network-based distributed real-time control and critical infrastructure systems. Integration of dedicated control protocols with wide area networks (e.g. the Internet). Issues of reliability, cost, and security. Application to selected industries, such as electric power distribution and waste and water management. Recommended preparation: ECE 340, CS 240, ME 313, CE 330, or CE 372. (Spring, alt/years.)
Prereq: Senior or Graduate standing in the College of Engineering

CS 445 Compiler Design

4 credits
Algorithms used by the following system software: assemblers, macro-processors, interpreters, and compilers; compiler design options and code optimization; all concepts implemented in major programming assignments.
Prereq: CS 210 and CS 385

CS 447 Computer and Network Forensics

3 credits
Joint-listed with CS 547
Competence in using established forensic methods in the handling of electronic evidence; rigorous audit/logging and date archival practices; prevention, detection, apprehension, and prosecution of security violators and cyber criminals; identifying and correcting computer vulnerabilities in a way that is smart, prudent, and responsible. Additional work required for graduate credit.
Prereq: CS 336 and Permission

CS 448 Survivable Systems and Networks

3 credits
Joint-listed with CS 548
Computers and networks under malicious threat or attack. Attributes of survivability, trustworthiness, dependability and assurance. Threats to survivability, security, reliability and performance. Models and analytical methods to assess survivability, vulnerability, interdependence and risk. Systemic inadequacies and approaches for overcoming deficiencies. Literature review and case studies. Additional projects/assignments required for graduate credit. Recommended Preparation: CS 449, CS 549 or CS 438.

CS 449 Fault-Tolerant Systems

3 credits
Joint-listed with CS 549. Cross-listed with ECE 449.
Design, modeling, analysis and integration of hardware and software to achieve dependable computing systems employing on-line fault tolerance; theory and fundamental concepts of designing reliable systems; analytical evaluation techniques, faults and advances in ultra-reliable distributed systems, fault-tolerant software systems; case studies include the space Shuttle, Airbus, and Boeing fly-by-wire primary flight computers as well as systems in reliable data bases and financial markets. Additional projects and assignments required for graduate credit.
Prereq: CS 240 or Permission

CS 451 Advanced Computer Architecture

3 credits
Joint-listed with CS 551. Cross-listed with ECE 441, ECE 541.
Principles and alternatives in instruction set design; processor implementation techniques, pipelining, parallel processors, memory hierarchy, and input/output; measurement of performance and cost/performance trade-off. Additional work required for graduate credit.
Prereq: CS 150, STAT 301 or Permission

CS 452 Real-Time Operating Systems

3 credits
Joint-listed with CS 552
Topics of interest in the implementation of Real-Time Operating Systems, especially as applicable to embedded systems, including a relevant hardware review, interrupts and interrupt handling, real-time scheduling principles and implementation, latency, task management, shared data and synchronization, timers, message passing, tradeoffs between memory space and speed. Students will build a simple but relatively complete real-time operating system over the course of the semester. Additional projects and assignments required for graduate credit. (Spring only)
Prereq: CS 240

CS 453 Advanced Robotics I

3 credits
Joint-listed with CS 553
The course studies the fundamentals of robotics/mechatronics systems and associated artificial intelligence applications. Topics to be covered include: principles of distributed systems control, interfacing and signal conditioning of sensors and actuators, data acquisition and signal processing, microprocessor-based control, physical modeling, and hardware and software simulation for model validation and control.
Prereq: Instructor Permission

CS 454 Advanced Robotics II

3 credits
Joint-listed with CS 554
The course continues the study of fundamentals of robotics/mechatronics operating systems and associated specific artificial intelligence applications in Robotics. Topics to be covered include: simulation of mixed environment robotic systems for model validation and control, interfacing and signal conditioning of sensors and actuators, and data acquisition and signal processing. Software architectures utilizing the ROS (Robotic Operating System) will be implemented and demonstrated on the appropriate physical robots and for associated remote computer-based sensors during the course.
Prereq: Instructor permission

CS 460 Database Management Systems Design

3 credits
Joint-listed with CS 560
Theory, analysis and implementation of database architecture, security, performance, query optimization, recovery and concurrency control, reliability, integrity, commit protocols, distributed processing, deadlock detection and management. Additional projects/assignments required for graduate credits.
Prereq: CS 360

CS 470 Artificial Intelligence

3 credits
Joint-listed with CS 570
Concepts and techniques involved in artificial intelligence, Lisp, goal-directed searching, history trees, inductive and deductive reasoning, natural language processing, and learning. Extra term paper required for graduate credit.
Prereq: CS 210

CS 472 Evolutionary Computation

3 credits
Joint-listed with CS 572
Solving computation problems by "growing" solutions; simulates natural evolution using analogues of mutation, crossover, and other generic transformations on representations of potential solutions; standard EC techniques such as genetic algorithms and evolutionary programming, mathematical explanations of why they work, and a survey of some applications; the focus is on solving real-world problems using projects. Graduate-level research and possible paper or presentation required for graduate credit.
Prereq: CS 210

CS 474 Deep Learning

3 credits
Joint-listed with CS 574
Deep Learning is enabling many rapid technological advances across multiple science disciplines, from automated speech recognition through medical image analysis and to autonomous robots and vehicles. This course will cover Deep Learning topics on gradient decent (GD), cross-validation, regularization, deep feedforward neural networks (NNs), convolutional NNs (CNNs), recurrent NNs (RNNs), deep architectures, transfer learning, and multitask learning. In this course students will learn to: understand and describe concepts and implementations of: deep forward networks, regularization, CNNs, RNNs, and transfer learning; apply CNNs and RNNs for modeling, analyzing, and solving real-world problems; select and apply adequate or best-fit toolboxes to train, tune, and test a deep neural network. Students will also gain an ability to successfully communicate, collaborate, and lead within a project group setting. Additional work required for graduate credit.
Prereq: (CS 121 or MATH 330) and STAT 301

CS 475 Machine Learning

3 credits
Joint-listed with CS 575.
Analysis and implementation of classic machine learning algorithms including neural networks, deep learning networks, principle component analysis, decision trees, support vector machines, clustering, reinforcement learning, ensemble learning, K-means, self-organizing maps and probabilistic learning such as Markov Chain Monte Carlo and Expectation Maximization algorithms. Techniques of pre-processing data, training, testing, and validating will be discussed along with statistical measures commonly used and pitfalls commonly encountered. Additional work required for graduate credit.
Prereq: CS 210

CS 477 Python for Machine Learning

3 credits
Joint-listed with CS 577
Python is widely used for Machine Learning and Data Science. This course introduces students to current approaches and techniques for finding solutions to Data Science problems using Machine Learning with Python. Topics include: classification, regression, clustering, ensemble learning, and deep learning. The course offers hands-on experiences with Machine Learning techniques using Python-based libraries and also modern tools used by computer and data scientists such as Jupyter Notebook. In this course students will learn: an ability to understand and describe the fundamental concepts and techniques of Machine Learning and their Python-based implementations; an ability to design, implement, and evaluate Python-based Machine Learning solutions for problems such as data classification and clustering. Students will also develop leadership and teamwork abilities through group discussions and projects. Additional work required for graduate credit.
Prereq: (CS 121 or MATH 330) and STAT 301

CS 479 Data Science

3 credits
Joint-listed with CS 579
Data science is advancing the conduct of science in individual and collaborative works. Data science combines aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. Key methodologies in application areas based on real research experience are taught to build a skill-set that enables students to handle each stage in a data life cycle, from data collection, analysis, archiving, to data discovery, access and reuse. Additional work required for graduate credit.
Prereq: MATH 330 or Permission

CS 480 CS Senior Capstone Design I

3 credits
Capstone design sequence for computer science majors. Formal development techniques applied to definition, design, coding, testing, and documentation of a large software project. Projects are customer-specified, includes real-world design constraints, and usually encompasses two semesters. Students work in teams. Significant lab work required.
Prereq: CS 383, ENGL 317, and Senior standing

CS 481 CS Senior Capstone Design II

3 credits
Gen Ed: Senior Experience
Continuation of CS 480. Application of formal design techniques to development of a large computer science project performed by students working in teams. Significant lab work required.
Prereq: CS 480

CS 489 Semantic Web and Open Data

3 credits
Joint-listed with CS 589
The Semantic Web extends the core principles of the World Wide Web to make the meaning of data machine-readable. This course covers the technological framework and associated functionalities enabled by the Semantic Web and Linked Open Data that provide a space for large scale data integration, reasoning and analysis. In this course students will learn: an ability to understand and describe the fundamental concepts in Semantic Web, such as ontology, RDF, OWL, logic reasoning, ontology engineering, knowledge graph, Linked Data, SPARQL, Open Data, as well as the inter-relationships among those concepts; an ability to design and implement domain-specific solutions for Big Data problems using concepts such as ontology engineering, data querying, analysis, and transformation, and output generation; an ability to describe and apply ethical concepts such as privacy, intellectual property, and responsibility as they relate to data analysis and the Semantic Web. Students will also develop leadership and teamwork abilities through group projects. Additional work required for graduate credit.
Prereq: CS 360 or CS 479 or CS 579

CS 499 (s) Directed Study

Credit arranged

CS 500 Master's Research and Thesis

Credit arranged

CS 501 (s) Seminar

Credit arranged

CS 502 (s) Directed Study

Credit arranged

CS 504 (s) Special Topics

Credit arranged

CS 505 (s) Professional Development

Credit arranged

CS 507 Computer Science Research Methods

3 credits
Introduction to Computer Science Research Methods for Graduate Students. Reading and writing research papers, experimental design, statistical analysis, responsible conduct of research, best practices in Computer Science research.

CS 510 Programming Language Theory

3 credits
Advanced topics in programming language theory including formal syntax, formal semantics, denotational semantics, and type theory; principles of programming language design are stressed; not a comparative language class. Cooperative: open to WSU degree-seeking students.
Coreq: CS 385 or equivalent

CS 511 Parallel Programming

3 credits
Joint-listed with CS 411
Analysis, mapping, and the application of parallel programming software to high-performance systems; the principles of spatial- and temporal-locality of data memory hierarchies in performance tuning; architectural considerations in the design and implementation of a parallel program; the tradeoff between threaded (shared memory) and message-passing (distributed memory) programming styles and performance. Additional projects/assignments required for graduate credit. Recommended Preparation: Proficiency in programming using a modern language such as C or C++.
Prereq: CS 395

CS 512 Parallel Algorithms

3 credits
Parallel algorithm design; formal analysis of parallel algorithmic complexity; measures of parallel efficiency; relationship between algorithmic structure and parallel mapping strategies; the consequences of spatial- and temporal-locality. Additional projects/assignments required for graduate credit.
Prereq: CS 395

CS 515 Computational Biology: Sequence Analysis

3 credits
Joint-listed with CS 415
Design and analyze algorithms that address the computational problems posed by biological sequence data, such as DNA or protein sequences. Topics may include: comparing sequences (from genes to genomes), database searching, multiple sequence alignment, phylogenetic inferencing, gene discovery and annotation, and genome assembly. Additional class presentation and/or paper required for graduate credit.
Prereq: Knowledge of high level programming language, basic probability theory, basic molecular biology; or Permission

CS 520 Data Communication Systems

3 credits
Joint-listed with CS 420.
Concept and terminology of data communications, equipment, protocols (including ISO/OSI and TCP/IP), architectures; transmission alternatives, regulatory issues and network management. Additional projects/assignments required for graduate credit.
Prereq: CS 150 and CS 240

CS 528 Multi-User Games and Virtual Environments

3 credits
Joint-listed with CS 428.
Software design and programming issues involved in constructing multi-user computer games and virtual environments, incorporating networking and 3D graphics. Additional projects and assignments required for graduate credit.
Prereq: CS 210, CS 324, and CS 328

CS 531 (s) SFS Professional Development

3 credits, max arranged
Joint-listed with CS 431.
This course is reserved for CyberCorps(R) Scholarship for Service program participants.
Prereq: Instructor Permission

CS 536 Advanced Information Assurance Concepts

3 credits
Advanced topics in design and analysis of network, database, and operating system security; current trends and research in mandatory and discretionary security policies. Recommended preparation: CS 336.

CS 538 Network Security

3 credits
Joint-listed with CS 438.
Practical topics in network security; policy and mechanism, malicious code; intrusion detection, prevention, response; cryptographic techniques for privacy and integrity; emphasis on trade-offs between risk of misuse, cost of prevention, and societal issues; concepts implemented in programming assignments. Additional projects/assignments required for graduate credit. Recommended Preparation: Knowledge of C or C++. CS 438 is cooperative: open to WSU degree-seeking students.
Prereq: CS 336

CS 539 Applied Security Concepts

3 credits
Joint-listed with CS 439.
Hands-on approach to computer security with emphasis on developing practical knowledge of how cyber attacks work and how to defend against them. Detailed exploration of attacks such as buffer overruns, string attacks, worms, trojan horses, and denial-of-service attacks, and development of defenses against them. Additional work required for graduate credit. Recommended preparation: Good knowledge of C, operating system concepts and Unix.
Prereq: CS 336 or Permission

CS 541 Advanced Operating Systems

3 credits
Joint-listed with CS 441
Principles of contemporary operating systems for network and distributed computer systems; sequential processes, scheduling, process synchronization, device management, file systems, memory management, and protection and security. Additional work required for graduate credit.
Prereq: CS 240

CS 543 Embedded Systems

3 credits
Joint-listed with CS 543
Interfacing to an embedded system processor. Development of the processor’s hardware-software interface. Application software development. Use of C and assembly language in device driver design, monitor-debugger, and real-time kernel. Regular laboratory assignments. (Fall only)
Prereq: CS 383

CS 544 Supervisory Control and Critical Infrastructure Systems

1 credit
Joint-listed with CS 444, Cross-listed with ECE 544
Principles of network-based distributed real-time control and critical infrastructure systems. Integration of dedicated control protocols with wide area networks (e.g. the Internet). Issues of reliability, cost, and security. Application to selected industries, such as electric power distribution and waste and water management. Recommended preparation: ECE 340, CS 240, ME 313, CE 330, or CE 372. (Spring, alt/years)
Prereq: Senior or Graduate standing in the College of Engineering

CS 547 Computer and Network Forensics

3 credits
Joint-listed with CS 447
Competence in using established forensic methods in the handling of electronic evidence; rigorous audit/logging and date archival practices; prevention, detection, apprehension, and prosecution of security violators and cyber criminals; identifying and correcting computer vulnerabilities in a way that is smart, prudent, and responsible. Additional work required for graduate credit.
Prereq: CS 336 and Permission

CS 548 Survivable Systems and Networks

3 credits
Joint-listed with CS 448
Computers and networks under malicious threat or attack. Attributes of survivability, trustworthiness, dependability and assurance. Threats to survivability, security, reliability and performance. Models and analytical methods to assess survivability, vulnerability, interdependence and risk. Systemic inadequacies and approaches for overcoming deficiencies. Literature review and case studies. Additional projects/assignments required for graduate credit. Recommended Preparation: CS 449, CS 549 or CS 438.

CS 549 Fault/Tolerant Systems

3 credits
Joint-listed with CS 449, Cross-listed with ECE 449
Design, modeling, analysis and integration of hardware and software to achieve dependable computing systems employing on-line fault tolerance; theory and fundamental concepts of designing reliable systems; analytical evaluation techniques, faults and advances in ultra-reliable distributed systems, fault-tolerant software systems; case studies include the space Shuttle, Airbus, and Boeing fly-by-wire primary flight computers as well as systems in reliable data bases and financial markets. Additional projects and assignments required for graduate credit.
Prereq: CS 240 or Permission

CS 551 Advanced Computer Architecture

3 credits
Joint-listed with CS 551, Cross-listed with ECE 541
Principles and alternatives in instruction set design; processor implementation techniques, pipelining, parallel processors, memory hierarchy, and input/output; measurement of performance and cost/performance trade-off. Additional work required for graduate credit.
Prereq: CS 150, STAT 301 or Permission

CS 552 Real Time Operating Systems

3 credits
Joint-listed with CS 452
Topics of interest in the implementation of Real-Time Operating Systems, especially as applicable to embedded systems, including a relevant hardware review, interrupts and interrupt handling, real-time scheduling principles and implementation, latency, task management, shared data and synchronization, timers, message passing, trade-offs between memory space and speed. Students will build a simple but relatively complete real-time operating system over the course of the semester. Additional projects and assignments are required for graduate credit. (Spring only)
Prereq: CS 240

CS 553 Advanced Robotics I

3 credits
Joint-listed with CS 453
The course studies the fundamentals of robotics/mechatronics systems and associated artificial intelligence applications. Topics to be covered include: principles of distributed systems control, interfacing and signal conditioning of sensors and actuators, data acquisition and signal processing, microprocessor-based control, physical modeling, and hardware and software simulation for model validation and control.
Prereq: Instructor Permission

CS 554 Advanced Robotics II

3 credits
Joint-listed with CS 454
This course continues the study of fundamentals of robotics/mechatronics operating systems and associated specific artificial intelligence applications in Robotics. Topics to be covered include: simulation of mixed environment robotic systems for model validation and control, interfacing and signal conditioning of sensors and actuators, and data acquisition and signal processing. Software architectures utilizing the ROS (Robotic Operating System) will be implemented and demonstrated on the appropriate physical robots and for associated remote computer-based sensors during the course.
Prereq: Instructor permission

CS 560 Database Management Systems Design

3 credits
Joint-listed with CS 460
Theory, analysis and implementation of database architecture, security, performance, query optimization, recovery and concurrency control, reliability, integrity, commit protocols, distributed processing, deadlock detection and management. Additional projects/assignments required for graduate credit.
Prereq: CS 360

CS 570 Artificial Intelligence

3 credits
Joint-listed with CS 470
Concepts and techniques involved in artificial intelligence, Lisp, goal-directed searching, history trees, inductive and deductive reasoning, natural language processing, and learning. Extra term paper required for graduate credit.
Prereq: CS 210

CS 572 Evolutionary Computation

3 credits
Joint-listed with CS 472
Solving computation problems by "growing" solutions; simulates natural evolution using analogues of mutation, crossover, and other generic transformations on representations of potential solutions; standard EC techniques such as genetic algorithms and evolutionary programming, mathematical explanations of why they work, and a survey of some applications; the focus is on solving real-world problems using projects. Graduate-level research and possible paper or presentation required for graduate credit.
Prereq: CS 210

CS 574 Deep Learning

3 credits
Joint-listed with CS 474
Deep Learning is enabling many rapid technological advances across multiple science disciplines, from automated speech recognition through medical image analysis and to autonomous robots and vehicles. This course will cover Deep Learning topics on gradient decent (GD), cross-validation, regularization, deep feedforward neural networks (NNs), convolutional NNs (CNNs), recurrent NNs (RNNs), deep architectures, transfer learning, and multitask learning. In this course students will learn to: understand and describe concepts and implementations of: deep forward networks, regularization, CNNs, RNNs, and transfer learning; apply CNNs and RNNs for modeling, analyzing, and solving real-world problems; select and apply adequate or best-fit toolboxes to train, tune, and test a deep neural network. Students will also gain an ability to successfully communicate, collaborate, and lead within a project group setting. Additional work required for graduate credit.
Prereq: (CS 121 or MATH 330) and STAT 301

CS 575 Machine Learning

3 credits
Joint-listed with CS 475
Analysis and implementation of classic machine learning algorithms including neural networks, deep learning networks, principle component analysis, decision trees, support vector machines, clustering, reinforcement learning, ensemble learning, K-means, self-organizing maps and probabilistic learning such as Markov Chain Monte Carlo and Expectation Maximization algorithms. Techniques of preprocessing data, training, testing, and validating will be discussed along with statistical measures commonly used and pitfalls commonly encountered. Additional work required for graduate credit.
Prereq: CS 210

CS 577 Python for Machine Learning

3 credits
Joint-listed with CS 477
Python is widely used for Machine Learning and Data Science. This course introduces students to current approaches and techniques for finding solutions to Data Science problems using Machine Learning with Python. Topics include: classification, regression, clustering, ensemble learning, and deep learning. The course offers hands-on experiences with Machine Learning techniques using Python-based libraries and also modern tools used by computer and data scientists such as Jupyter Notebook. In this course students will learn: an ability to understand and describe the fundamental concepts and techniques of Machine Learning and their Python-based implementations; an ability to design, implement, and evaluate Python-based Machine Learning solutions for problems such as data classification and clustering. Students will also develop leadership and teamwork abilities through group discussions and projects. Additional work required for graduate credit.
Prereq: (CS 121 or MATH 330) and STAT 301

CS 578 Neural Network Design

3 credits
Cross-listed with ECE 578 and ME 578
Introduction to neural networks and problems that can be solved by their application; introduction of basic neural network architectures; learning rules are developed for training these architectures to perform useful functions; various training techniques employing the learning rules discussed and applied; neural networks used to solve pattern recognition and control system problems.
Prereq: Permission

CS 579 Data Science

3 credits
Joint-listed with CS 479
Data science is advancing the conduct of science in individual and collaborative works. Data science combines aspects of data management, library science, computer science, and physical science using supporting cyber-infrastructure and information technology. Key methodologies in application areas based on real research experience are taught to build a skill-set that enables students to handle each stage in a data life cycle, from data collection, analysis, archiving, to data discovery, access and reuse. Additional work required for graduate credit.
Prereq: MATH 330 or Permission

CS 580 Graduate Project

1-6 credits, max 6
Application of formal design and documentation techniques to the development of computer programming project; project selected in consultation with student's major professor.
Prereq: CS 383, CS 480 or Permission

CS 589 Semantic Web and Open Data

3 credits
Joint-listed with CS 489
The Semantic Web extends the core principles of the World Wide Web to make the meaning of data machine-readable. This course covers the technological framework and associated functionalities enabled by the Semantic Web and Linked Open Data that provide a space for large scale data integration, reasoning and analysis. In this course students will learn: an ability to understand and describe the fundamental concepts in Semantic Web, such as ontology, RDF, OWL, logic reasoning, ontology engineering, knowledge graph, Linked Data, SPARQL, Open Data, as well as the inter-relationships among those concepts; an ability to design and implement domain-specific solutions for Big Data problems using concepts such as ontology engineering, data querying, analysis, and transformation, and output generation; an ability to describe and apply ethical concepts such as privacy, intellectual property, and responsibility as they relate to data analysis and the Semantic Web. Students will also develop leadership and teamwork abilities through group projects. Additional work required for graduate credit.
Prereq: CS 360 or CS 479 or CS 579

CS 598 (s) Internship

Credit arranged

CS 599 (s) Non-thesis Master's Research

Credit arranged
Research not directly related to a thesis or dissertation. There is a limit on the number of credits in 599 that can be included on a study plan.
Prereq: Permission

CS 600 Doctoral Research and Dissertation

Credit arranged