Machine Learning for Engineering Applications, Certificate
Associate Dean for Academic Affairs: Mark Federle, Ph.D.
Master's Across Boundaries Certificates Programs webpage
DEGREE OFFERED
Certificate
PROGRAM DESCRIPTION
By completing the graduate certificate in machine learning for engineering applications, students develop the capabilities required to apply current tools and appropriate approaches to solve complex problems in a variety of domains. Students gain a greater technical understanding of the elements of machine learning, including algorithms, intelligent systems, neural networks, pattern recognition and deep learning.
Faculty Sponsor: Richard Povinelli, Ph.D.
The graduate certificate in machine learning for engineering applications (MLRN) is intended to develop the capabilities required to apply current tools and approaches to solve complex machine learning problems in a variety of application domains. The certificate offers students the opportunity to achieve a greater technical understanding of the elements of machine learning, which includes algorithms, intelligent systems, neural networks, pattern recognition and deep learning. In addition, students understand the possibilities and limitations of machine learning for a variety of industry applications such as image processing, medical diagnostics, wastewater treatment, cyber defense and machine finishing.
The certificate in machine learning requires four courses for a total of 12 credit hours. The 12 credits of course work are proposed by a student in consultation with the program director or academic adviser. All certificate programs meet the requirements in the Graduate School Bulletin, including any course prerequisite requirements. Any deviations from the approved courses in the Graduate School Bulletin or course substitutions must be approved by the Certificate Faculty Sponsor, the EECE director of graduate studies (DGS), and the chair of the EECE department. This certificate is designed for practicing professionals. As such, it is anticipated that only the part-time option is likely to be pursued.
| Code | Title | Hours |
|---|---|---|
| Required Course: | ||
| EECE 6822 | Machine Learning | 3 |
| Elective courses - choose three from the following: | 9 | |
| Visual Analytics | ||
| Data Mining and Machine Learning | ||
| Advanced Machine Learning | ||
| Data at Scale | ||
| Data Ethics | ||
| Introduction to Algorithms | ||
| Developments in Computer Software | ||
or COSC 5600 | Fundamentals of Artificial Intelligence | |
| Introduction to Intelligent Systems | ||
| Introduction to Neural Networks and Fuzzy Systems | ||
| Evolutionary Computation | ||
| Algorithm Analysis and Applications | ||
| Artificial Intelligence | ||
| Pattern Recognition | ||
| Neural Networks and Neural Computing | ||
| Advanced Topics in Electrical and Computer Engineering | ||
Other courses as approved by the Certificate Faculty Sponsor, the EECE director of graduate studies (DGS) and the chair of the EECE department. | ||
| Total Credit Hours: | 12 | |
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COSC 5010 Teaching Computer Science (3 credits)
Historical background, problems, curricular materials and pedagogy in computer science pertinent to the needs of secondary school teachers. Consent required.
Level of Study: Graduate
COSC 5290 Real-Time and Embedded Systems (3 credits)
Focuses on event-driven programming, real-time scheduling, and synchronization; worst-case execution time analysis and deadline analysis; real-time operating systems and real-time programming languages.
Level of Study: Graduate
COSC 5300 Network Design and Security (3-4 credits)
Focuses on the design and protocols of the upper layers of the Internet architecture, including the TCP/IP protocol suite, packet switching and routing, network programming and applications. Emphasizes related security attacks and defenses, including DNSSEC, TSL, IPsec and the BGP PKI protections. Taught as a lecture only or as a lecture with lab component.
Level of Study: Graduate
COSC 5360 Software and System Security (3 credits)
Fosters comprehension of cybersecurity foundations such as cryptography, operating system security, threat modeling, and secure programming. Develops passion for cybersecurity essential to performance of professional roles as developers, engineers, and managers.
Level of Study: Graduate
COSC 5370 Internet of Things (IoT) (3 credits)
Topics include the definition of IoT, trends in the adoption of IoT, the importance of the IoT in society, the current components of typical IoT devices and trends for the future. Focuses on IoT design considerations, constraints, and interfacing between the physical world and the device. Students are presented with design trade-offs between hardware and software, technologies behind the Internet of Things – RFID, NFC, Wireless networks, WSN, RTLS, GPS, agents, multiagent systems, IoT in retail, NFC applications for the IoT, and IoT in healthcare.
Level of Study: Graduate
COSC 5380 Web Development (3 credits)
Introduction to web development technologies such as HTML, CSS, JavaScript, jQuery, SQL. Understand and apply different client-side or front-end and middleware technologies. Gain practical experience in developing server-side and backend web applications.
Level of Study: Graduate
COSC 5400 Compiler Construction (3 credits)
Lexical analysis, parsing, code generation and optimization. Includes theoretical foundations and the practical concerns of implementation.
Level of Study: Graduate
COSC 5500 Visual Analytics (3 credits)
Focuses on developing data products using the Javascript/D3 framework by combining concepts from human-computer interaction, visualization and design. Also focuses on model visualization, interpretation, A/B testing and design thinking.
Level of Study: Graduate
COSC 5510 User-Interface Design and Evaluation (3 credits)
An introduction to the field of human-computer interaction (HCI), with a focus on user interface design, implementation, and evaluation. Learn how to design good user interfaces, covering important design principles and the human capabilities that motivate them. Learn techniques for building user interfaces, including low and high-fidelity prototypes, Wizard of Oz, and other prototyping tools. Evaluate and measure interface usability, including heuristic evaluation, and user testing.
Level of Study: Graduate
COSC 5550 Social and Collaborative Computing (3 credits)
Introduces Social Computing and Computer-Supported Cooperative Work (CSCW). Field includes theory, technology and study of computing systems that support groups of users and facilitate collaboration. Example applications and case studies include email, social networking sites, peer production, crowdsourcing, calendars, scheduling and meeting apps, online dating sites, multiplayer games, discussion forums, instant messaging, collaborative editors and analysis tools.
Level of Study: Graduate
COSC 5600 Fundamentals of Artificial Intelligence (3 credits)
An introduction to the broad field of artificial intelligence. Topics include problem solving by searching, knowledge representation, reasoning, planning, decision making, learning, perception and language processing.
Level of Study: Graduate
Interdisciplinary Studies: Cognitive Science
COSC 5610 Data Mining and Machine Learning (3 credits)
Data mining and machine learning techniques for extracting and evaluating patterns from large databases. Introduction to knowledge discovery process. Fundamental tasks including classification, prediction, clustering, association analysis, summarization and discrimination. Basic techniques including decision trees, neural networks, statistics, partitional clustering and hierarchical clustering.
Level of Study: Graduate
Interdisciplinary Studies: Bioinformatics
COSC 5800 Principles of Database Systems (3 credits)
Topics include database concepts and architecture, data modeling, formal query languages such as relational algebra, commercial query language SQL, database access from application programs and a brief examination of advanced concepts including transactions, distributed databases, security and XML.
Level of Study: Graduate
Interdisciplinary Studies: Bioinformatics
COSC 5860 Component-Based Software Construction (3 credits)
Introduction to software components in the context of the object-oriented paradigm. Component development, component selection and adaptation/customization, component deployment and assembly/integration, and system architecture. Industry standards such as JavaBeans, CORBA Component Model, and Microsoft COM/DOM/COM+.
Level of Study: Graduate
COSC 5931 Topics in Computer Science (1-3 credits)
Topics selected from one of the various branches of computer science. Specific topics to be announced in the Schedule of Classes.
Level of Study: Graduate
COSC 6050 Elements of Software Development (3 credits)
Students explore the software design and development processes through a term project. Concepts covered include: requirements gathering and analysis, mapping requirements to a design, sound coding and documentation practices, configuration management, testing and quality assurance, system deployment and maintenance.
Prerequisite: Programming in a high-level language, knowledge in data structures such as stacks, recursion, queues, trees and graphs.
Level of Study: Graduate
COSC 6051 Professional Software Engineering 1 (3 credits)
Covers software engineering topics typically including: the software development life cycle (SDLC), development methodologies, software quality overview, configuration management, designing for risks and fault tolerance, languages and design, object-oriented programming, observational research and prototyping, requirements, software architectures, operating systems design and real time systems. Offered at General Electric facilities. As this course extends beyond the Marquette term, students receive the grade of IC initially. The IC grade converts to an A-F grade at the completion of the course.
Prerequisite: GE employee in the Software Edison program.
Level of Study: Graduate
COSC 6052 Professional Software Engineering 2 (3 credits)
Covers software engineering topics typically including: systems and communication networks, security and distributed systems, interoperability and standards, design for "ility" (e.g., usability and reliability) and performance, design for parallel processing, embedded systems hardware for software developers, embedded systems software, software design patterns and algorithms. Offered at General Electric facilities. As this course extends beyond the Marquette term, students receive the grade of IC initially. The IC grade converts to an A-F grade at the completion of the course.
Prerequisite: GE employee in the Software Edison program.
Level of Study: Graduate
COSC 6053 Professional Software Engineering 3 (3 credits)
Covers software engineering topics typically including: database systems, decision science, data quality and analytics, user interface design, design for globalization, debugging and troubleshooting, approach, method, implementation and emerging software technologies. Offered at General Electric facilities. As this course extends beyond the Marquette term, students receive the grade of IC initially. The IC grade converts to an A-F grade at the completion of the course.
Prerequisite: GE employee in the Software Edison program.
Level of Study: Graduate
COSC 6054 Professional Software Engineering 4 (3 credits)
Covers design topics related to system design with embedded computing. Topics typically include: design of controls, design for low cost, design for serviceability, design for usability, design for reliability, program management, innovation, requirements management and design thinking. Offered at General Electric facilities. As this course extends beyond the Marquette term, students receive the grade of IC initially. The IC grade converts to an A-F grade at the completion of the course.
Prerequisite: GE employee in the Software Edison program.
Level of Study: Graduate
COSC 6055 Software Quality Assurance (3 credits)
Provides a perspective on people, organizations, controls, processes and tools that collectively influence the success of a Software Quality Assurance (SQA) strategy. Discussion topics include quality approaches as they apply to: requirements, design, release, configuration management, testing, defect management, operations and support. Topics are discussed in the context of a traditional development approach (waterfall, CMMI) and more contemporary models driven by lean and agile practices. Covers considerations specific to implementing an SQA approach within a regulated setting. Approach emphasizes a hands-on view of SQA, thereby providing realistic takeaways to practice in a professional career.
Level of Study: Graduate
COSC 6060 Distributed and Cloud Computing (3 credits)
Students use and develop software for distributed and cloud computing systems. Topics include job submission and management, tools distributed software development, approaches for implementing distributed and cloud computation, virtualization, public cloud platforms, cloud data management, cloud security and privacy.
Prerequisite: COSC 3100 or equiv.
Level of Study: Graduate
COSC 6090 Research Methods/Professional Development (1 credits)
Designed to introduce the process of research and communication of research in computer science, including presentation and publication of research, preparation of grant proposals, and ethical considerations. May be repeated.
Level of Study: Graduate
COSC 6260 Advanced Algorithms (3 credits)
Covers advanced paradigms for the design and analysis of efficient algorithms. Emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Domains include: string algorithms, network optimization, parallel algorithms, computational geometry, external memory and streaming algorithms, and advanced data structures.
Level of Study: Graduate
COSC 6270 Advanced Operating Systems (3 credits)
Fundamental concepts of operating systems including kernel data structures; process control and scheduling; interprocess communication and synchronization; virtual memory and memory management; mass storage systems and device control; protection and security; and protection and virtualization; evaluation and prediction of performance. Students are expected to spend at least three hours per week gaining hands-on experience in using and modifying a small operating system.
Level of Study: Graduate
COSC 6280 Advanced Computer Security (3 credits)
Symmetric key and public key cryptography, hash functions, random numbers and cryptanalysis; authentication and authorization, password-based security, ACLs and capabilities, covert channels, security models, firewalls and intrusion detection systems; authentication protocols, session keys, SSH, SSL, IPSec, Kerberos, WEP, and GSM; flaws and malware, buffer overflows, viruses and worms, malware detection, software reverse engineering, digital rights management, secure software development and operating systems security; fundamentals about bitcoin and cryptocurrency technologies. Students write programs for assignments using the C programming language.
Level of Study: Graduate
COSC 6330 Advanced Machine Learning (3 credits)
Provides a graduate-level introduction to machine learning and statistical pattern recognition and in-depth coverage of new and advanced methods in machine learning, as well as their underlying theory. Emphasizes approaches with practical relevance and discusses a number of recent applications of machine learning, such as data mining, computer vision, robotics, text and web data processing. An open research project is a major part of the course.
Level of Study: Graduate
COSC 6340 Component Architecture (3 credits)
Focuses on designing and implementing software components, and streamlining the translation from business intent into realized application behavior in a practical hands-on, business-based environment. Introduces service-oriented architecture (SOA) and principles such as loose coupling, abstraction, reusability, autonomy, statelessness, discoverability, interoperability and composability.
Level of Study: Graduate
COSC 6345 Mobile Health (mHealth) (3 credits)
Offers a multidisciplinary overview of the emerging technologies used in mobile health (mHealth). Research and innovations in this area promise solutions to the need for broader access to affordable and effective healthcare by enabling consumers and their caregivers to take charge of their health and well-being. mHealth is the provision of health information and services using sensor data via mobile phones and tablets. Students develop foundational knowledge of understanding the behaviors, different data models, security and privacy issues.
Level of Study: Graduate
COSC 6350 Distributed Computing (3 credits)
Introduces a broad spectrum of topics encompassing system architecture, software abstractions, distributed algorithms and issues pertaining to distributed environments such as replication, consistency, fault tolerance, transactions and security.
Level of Study: Graduate
COSC 6355 Mobile Computing (3 credits)
Focuses on the fundamentals of mobile computing, challenges in mobile computing, mobility management and mobile data management. Also focuses on context awareness and wireless communications, ubiquity of wireless communication technologies and standards, seamless access network services and resources from anywhere, at anytime, middleware for mobile computing, operation systems, programming languages, network protocols and security aspects of mobile computing. Explores concepts in sensor networks, including operating systems, programming languages, network protocols and programming models.
Prerequisite: COSC 2100 or equiv.
Level of Study: Graduate
COSC 6360 Enterprise Architecture (3 credits)
Focuses on key topics and concepts that represent enterprise architecture (EA). Addresses the people, process and technology elements of EA from both a business and technical perspective. Explores the background, history, planning, governing, maintaining and common methodologies associated with EA. Prototypes some of the technology used in enterprises today to gain a better understanding of how information is represented, systems are integrated and standards are put into practice.
Level of Study: Graduate
COSC 6375 Web Technologies (3 credits)
Exposes students to design and architectural principles in developing web applications. Focuses on the client side, middleware and service layer of web applications. Topics range from HTML, JavaScript, JQuery, Java Servlets, MVC Design Pattern, Java Spring MVC, SQL, JDBC, Hibernate, AngularJS and Cloud Computing.
Level of Study: Graduate
COSC 6380 Big Data Systems (3 credits)
Focuses on newer, advanced database techniques in the areas of Big Data, NoSQL, Hadoop and Apache Spark. Covers main NoSQL data management topics such as document databases, key-value stores and graph databases.
Prerequisite: Database Systems or equiv.
Level of Study: Graduate
COSC 6390 Professional Seminar in Computing (1 credits)
Topic to be chosen each term from among issues important to all professionals in computing. S/U grade assessment.
Level of Study: Graduate
COSC 6500 Foundations of Computing (6-7 credits)
Presents the breadth and current status of computer science in our computerized society and the fundamentals of professional knowledge, skills and abilities. Foundational topics are intermixed with study of software development which include an introduction to abstraction, algorithmic thinking, simulation and testing for computer-based problem solving using higher-level programming languages. Algorithm analysis and computational complexity are presented in the context of considering data structures, algorithms and alternatives. Students program exercises using graphical user interfaces, data base connections, parallel computing and interfaces to the World Wide Web (WWW). Experience includes using an interactive development environment, studying software development methodology, and testing code, basic system administration, computer networking and operating system configuration. Consent required.
Level of Study: Graduate
COSC 6510 Data Intelligence (3 credits)
Foundational topics in data intelligence. Includes properties and benefits for data intelligence and methodology for the development of data intelligence solutions. Examines technology employed for managing data and creating visualizations and dashboards. Topics include developing a business case, evaluating performance and managing data. Presents overview of data architectures commonly used in data intelligence solutions and includes exercises using common techniques for prediction and time series analysis.
Level of Study: Graduate
COSC 6520 Data Analytics (3 credits)
Foundational topics in the analysis of data. Includes methodology for the development of data analytics systems. Examines technology employed for data analytics in a variety of industry segments and the benefits derived from data analytics. Foundations of text and data mining techniques commonly used for classification, clustering and prediction. Students are presented techniques for developing a business case, evaluating predictive performance and managing data. Includes exercises using analytic technology and a project to apply analytics to a customer application. Students without programming experience are advised to complete COSC 6510 Data Intelligence before attempting COSC 6520.
Level of Study: Graduate
COSC 6530 Concepts of Data Warehousing (3 credits)
Provides an introduction to data warehouse design. Reviews topics in data modeling, database design and database access. Data warehouse planning, design, implementation and administration. The role of data warehouse in supporting decision support systems (DSS), business intelligence and business analytics.
Level of Study: Graduate
COSC 6550 Introduction to Cybersecurity (3 credits)
Provides an introduction to cybersecurity threats, methods and security techniques. Foundations of various cybersecurity frameworks and methods for applying them to different types of organizations. Includes cyber threat environment, along with methods, tools and techniques that can help mitigate vulnerabilities and reduce risks to an organization.
Level of Study: Graduate
COSC 6560 Principles of Service Management and System Administration (3 credits)
Introduction to the concepts, principles and practices involved in the operations of secure computing systems. Presents principles of service management and explores how the principles of system administration are derived from concepts of delivering quality services. Lab exercises performing rudimentary tasks of a system administrator using virtual machine environments. Foundation topics include: cryptography, popular operating systems for servers, network configuration, system components, networked systems, host management, user management, configuration of servers and services, incident management, change management, security, monitoring and analysis of operations.
Prerequisite: Basic knowledge of scripting, operating systems and services.
Level of Study: Graduate
COSC 6570 Data at Scale (3 credits)
Combines ideas from parallel databases, distributed systems and programming languages to analyze data at scale. Relevant technologies are introduced and taught in an accessible and inclusive way. Some examples include cloud computing, SQL and NoSQL databases, MapReduce ecosystem, Spark and its contemporaries and graph databases.
Level of Study: Graduate
COSC 6580 Data Security and Privacy (3 credits)
Focuses on fundamental and advanced topics in data security and privacy, including differential privacy, secure multi-party computation, homomorphic encryption, data perturbation, data anonymization, security and privacy in AI, location privacy, and social network privacy. Students also learn practical skills via projects.
Prerequisite: Basic knowledge on statistics, databases, machine learning/data mining, and distributed systems.
Level of Study: Graduate
COSC 6780 Human-Centered Computing (3 credits)
In-depth exploration of theories and methods used in the interdisciplinary field of Human-Computer Interaction (HCI). Delves into various qualitative and quantitative research methodologies, how to design and conduct experiments, and how to analyze and interpret data. Designed to enable students to critically evaluate HCI research and design rigorous empirical studies that contribute to the advancement of the field. Through a combination of readings, discussions, and hands-on projects, students gain both theoretical knowledge and practical skills necessary for advanced research in HCI.
Level of Study: Graduate
COSC 6820 Data Ethics (3 credits)
A comprehensive overview of the current ethical and social implications of our data-driven society. A sociotechnical approach is used to unpack issues of privacy and surveillance, algorithmic biases, fairness, transparency, and accountability across various contexts.
Level of Study: Graduate
COSC 6840 Ethical Hacking Theory and Practice (3 credits)
A comprehensive introduction to ethical hacking theory and principles. Emphasizing practical applications, students gain hands-on experience in network defense, ethical hacking, vulnerability assessment, cryptography, and digital forensics. Students also gain in-depth knowledge by analyzing theory and case studies that leverage more advanced techniques.
Level of Study: Graduate
COSC 6931 Topics in Computer Science (1-3 credits)
Topics vary. Students may enroll more than once as the subject matter changes.
Level of Study: Graduate
COSC 6960 Seminar in Computer Science (1-3 credits)
Seminar topics selected from one of the various branches of computer science. Specific topics to be announced in the Schedule of Classes.
Level of Study: Graduate
COSC 6964 Practicum for Research and Development in Computing (3-6 credits)
S/U grade assessment.
Prerequisite: 3.00 MU GPA; must be enrolled in Plan B option of the M.S. in computing program and have completed at least 15 credit hours earned in graduate (6000-level) courses. Available only to full-time students. Cons. of the computing dir. of graduate studies or cons. of dept. ch. Consent required.
Level of Study: Graduate
COSC 6965 Curriculum Integrated Practicum in Computing (1-2 credits)
Involves practical application of the knowledge and skills being studied concurrently, and previously studied, in other course work for computing professionals.
Prerequisite: Admission to the COMP program's integrated practicum option; cons. of the computing dir. of graduate studies or cons. of dept. ch.
Level of Study: Graduate
COSC 6974 Practicum for Research and Development in Computer Science (1-6 credits)
Students in the MS in Computing program should be registering for COSC 6964, Practicum for Research and Development in Computing. S/U grade assessment.
Prerequisite: Cons. of dept. ch. Consent required.
Level of Study: Graduate
COSC 6975 Curriculum Integrated Practicum in Computer Science (0 credits)
Involves advancing practical research and development of the computer science doctoral program student. S/U grade assessment.
Prerequisite: Admitted to the COSC-PHD prog.; cons. of co-op chair. Consent required.
Level of Study: Graduate
COSC 6995 Independent Study in Computer Science (1-6 credits)
An in-depth study on a topic or subject matter usually not offered in the established curriculum with faculty and independent of the classroom setting.
Prerequisite: Cons. of instr. and cons. of dept. ch. Consent required.
Level of Study: Graduate
COSC 6998 Professional Project in Computer Science (0 credits)
S/U grade assessment.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 6999 Master's Thesis (1-6 credits)
S/U grade assessment.
Prerequisite: Cons. of dept. ch. Consent required.
Level of Study: Graduate
COSC 8995 Independent Study in Computer Science (1-3 credits)
A doctorate level in-depth research on a topic or subject matter usually not offered in the established curriculum with faculty and independent of the classroom setting.
Prerequisite: Cons. of instr. and cons. of dept. ch. Consent required.
Level of Study: Graduate
COSC 8999 Doctoral Dissertation (1-12 credits)
S/U grade assessment.
Prerequisite: Cons. of dept. ch. Consent required.
Level of Study: Graduate
COSC 9970 Graduate Standing Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Designated as less than half-time status only, cannot be used in conjunction with other courses, and does not qualify students for financial aid or loan deferment.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9974 Graduate Fellowship: Full-Time (0 credits)
Fee. S/U grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9975 Graduate Assistant Teaching: Full-Time (0 credits)
Fee. S/U grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9976 Graduate Assistant Research: Full-Time (0 credits)
Fee. S/U grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9987 Doctoral Qualifying Examination Preparation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week toward their doctoral qualifying exam.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9988 Doctoral Qualifying Examination Preparation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week toward their doctoral qualifying exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9989 Doctoral Qualifying Examination Preparation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week toward their doctoral qualifying exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9991 Professional Project Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their professional project. Any professional project credits required for the degree should be completed before registering for non-credit Professional Project Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9992 Professional Project Continuation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their professional project. Any project credits required for the degree should be completed before registering for non-credit Professional Project Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9993 Professional Project Continuation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their professional project. Any professional project credits required for the degree should be completed before registering for non-credit Professional Project Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9994 Master's Thesis Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9995 Master's Thesis Continuation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9996 Master's Thesis Continuation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9997 Doctoral Dissertation Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9998 Doctoral Dissertation Continuation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
COSC 9999 Doctoral Dissertation Continuation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 5090 Developments in Electronics (1-3 credits)
Course content announced prior to each term. Students may enroll in the course more than once as subject matter changes. May be taught in traditional lecture format or as a seminar which focuses on readings from current literature. Possible topics include laser electronics, optoelectronics and photonics, RF circuit design, SOC design.
Prerequisite: Cons. of instr. or grad. stndg.
Level of Study: Graduate
EECE 5100 Transmission Lines and Electromagnetic Waves (3 credits)
Development and use of wave equations as derived from Maxwell’s equations to explain the propagation of electromagnetic waves. Includes wave propagation, reflection/diffraction, antennas, and transmission lines including use of the Smith chart. Discusses S parameters and the vector network analyzer. An introduction to the physical principles of radio communication.
Level of Study: Graduate
EECE 5110 Microwave Engineering (3 credits)
Studies the fundamentals of microwave engineering. After a review of transmission line theory and the Smith chart, the scattering parameters are developed and used to characterize and design a variety of devices including power dividers/directional couplers, filters, amplifiers, oscillators and mixers. Receiver architectures and system noise are also introduced and developed.
Level of Study: Graduate
EECE 5130 Antenna Theory and Design (3 credits)
Design and use of antennas of varying types, including wire, broadbands, horn, and reflector antennas in transmitting and receiving applications. The application and design of antenna arrays, and an introduction to diffraction theory.
Level of Study: Graduate
EECE 5150 Applied Finite Elements in Electromagnetics (3 credits)
Introduction to finite element (FE) analysis as applied to linear and static electromagnetic field problems. Review of basic field formulations using Maxwell's electromagnetic field equations, solution of boundary value problems using the finite difference methods, FE formulations, assembly of elemental and global matrices, pre-processing, post-processing. Application of the FE method using one-dimensional and two-dimensional elements, magnetostatic and electrostatic analysis, and the use of commercially available software packages.
Level of Study: Graduate
EECE 5190 Developments in Electromagnetics (1-3 credits)
Course content announced prior to each term. Students may enroll in the course more than once as subject matter changes. May be taught in traditional lecture format or as a seminar which focuses on readings from current literature. Possible topics include wireless and microwave components and systems, electromagnetic compatibility, radio wave propagation.
Prerequisite: Cons. of instr. or grad. stndg.
Level of Study: Graduate
EECE 5210 Design and Analysis of Electric Motor-Drive Systems (3 credits)
Principles of design of AC and DC electric machines, in particular design of electric motors in power electronically controlled adjustable speed drives, torque and power to volume analysis under constant volts per hertz torque-speed control. Covers design of AC induction, synchronous, universal and DC conventional as well as brushless DC motors, and low horsepower motors in adjustable speed drives. Covers effects of space and time harmonics on motor design and performance are covered including harmonic abatement for control of torque pulsation. Use of modern modeling techniques throughout.
Level of Study: Graduate
EECE 5220 Power Electronics for Renewable Energy Systems (3 credits)
Fundamental concepts, techniques, and methods for design and analysis of power electronic systems. Modeling of semiconductor switching devices for use in power electronic systems. Practical aspects and power electronic conversion techniques for rectifiers, DC-to-DC converters, DC-to-AC inverters and their applications in power electronic systems.
Level of Study: Graduate
EECE 5230 Renewable and Legacy Electric Energy Systems Analysis (3 credits)
Elements of renewable and legacy electric power systems; fundamental concepts and techniques for design and analysis; per unit system; load flow; economic dispatch; smart grids and load management; steady state and transient power system stability.
Level of Study: Graduate
EECE 5240 Protection and Monitoring of Electric Energy Systems (3 credits)
Principles of design of relay and sensor systems for detection of faulty operating conditions in electric generators, transformers, power transmission lines, motors and other loads in power systems. Symmetrical components, balanced and unbalanced faults including single and multiple unbalances. Design and hierarchical coordination of protection systems for interconnected generation, transmission and distribution facilities in power systems, which includes integrated generator-transformer-busbar-transmission line-load protection and analysis of operation under fault conditions.
Level of Study: Graduate
EECE 5250 Transients in Electric Energy Systems and Devices (3 credits)
Covers microsecond fast transients in power systems and devices resulting from lightning strokes, switching surges in power systems and devices, as well as impulse surges resulting from pulse width modulation in modern adjustable speed drives, using distributed parameter models and analysis of transmission lines and windings of transformers, generators and motors. Also covers successive reflections, transition points, wavefront flattening techniques and surge arrestor design applications for voltage buildup reduction and control are studied. Includes polyphase multi-velocity multi-conductor system transients.
Level of Study: Graduate
EECE 5290 Developments in Energy and Power (1-3 credits)
Course content is announced prior to each term. Students may enroll in the course more than once as subject matter changes. May be taught in traditional lecture format or as a seminar which focuses on readings from current literature. Topics may include: electronics for machine and drive systems, electrical transients, faults and diagnostics and protection in power devices and systems, renewable energy systems, smart grids and advanced topics in the electric energy engineering area.
Level of Study: Graduate
EECE 5310 Control Systems (3 credits)
Review of continuous-time linear systems. Time-domain system analysis. Time-domain design of lead/lag and PID controllers. Root-Locus technique. Frequency-domain system analysis including Nyquist, Bode, and Nichols analysis and relative stability. Frequency-domain design/lag and PID controllers.
Level of Study: Graduate
EECE 5320 Digital Control Systems (3 credits)
Review of sampling processes, discrete time linear systems analysis and Z-transforms. Discrete time and sampled data state-variable analysis. Stability analysis, time domain and frequency-domain analysis and design. Digital control design and implementation issues.
Level of Study: Graduate
EECE 5390 Developments in Control (1-3 credits)
Course content is announced prior to each term. Students may enroll in the course more than once because subject matter changes. Depending upon the subject matter and the instructor, the class may be taught in traditional lecture format or as a seminar which focuses on readings from the current literature. Possible topics include optimal, adaptive and robust control methods, digital control and nonlinear systems.
Level of Study: Graduate
EECE 5410 Introduction to Device Fabrication (3 credits)
Fundamentals of integrated circuit (IC) and semiconductor device fabrication technology. Also studies specialized microelectromechanical systems (MEMS) processing. Students develop an advanced understanding of all aspects of IC fabrication including: materials (Si, SiO2, GaAs, Al, Au, etc.), processes (deposition, etching, lithography, oxidation/diffusion, etc.), and equipment (reactive ion etching, evaporator, plasma sputtering, chemical vapor deposition, etc.). Includes both theoretical and experimental considerations.
Level of Study: Graduate
EECE 5430 Physical Principles of Solid State Devices (3 credits)
Presents fundamental physical principles of solid state devices. Includes a brief review of Quantum Mechanics and applications of modern semiconductor devices that use the Quantum Mechanics principles. Explains the operation principle of modern semiconductor devices from Quantum Mechanics, and these principles are used to extend the students’ knowledge of devices used in electronic circuits.
Level of Study: Graduate
EECE 5440 MEMS and Nanotechnology (3 credits)
Lecture and laboratory work are combined to provide students with a practical, hands-on introduction to micro-electro-mechanical systems (MEMS) and nano-electro-mechanical systems (NEMS).
Level of Study: Graduate
EECE 5460 Sensor Devices: Theory, Design and Applications (3 credits)
Sensor classification and transduction principles. Fundamental principles and theory of operation of various types of sensors, based on various technologies which include: optical, electrical, acoustical, thermal, magnetic, mechanical and chemical. Analysis of sensor signals. Study of sensor characteristics which include hysteresis, non-linearity, saturation, repeatability, sensitivity, selectivity and resolution. Design and practical implementations of various sensors for scientific, industrial and consumer applications.
Level of Study: Graduate
EECE 5490 Developments in Devices (1-3 credits)
Course content announced prior to each term. Students may enroll in the course more than once as subject matter changes. May be taught in traditional lecture format or as a seminar which focuses on readings from current literature. Possible topics include: optoelectronic devices, nano-scale devices, solid-state devices, integrated electronic devices, power devices, electro-mechanical devices, quantum devices.
Level of Study: Graduate
EECE 5510 Digital Signal Processing (3 credits)
Introduction to the theory and practice of discrete-time signals and systems. Concepts covered include Fourier Transforms, Z-transforms, linear time invariant system analysis in the time and frequency domains, sampling theory and Discrete Fourier Transforms. Application of these concepts includes digital filter design techniques and the use of Fast Fourier Transforms for efficient frequency domain analysis. Labs and design projects related to specific signal processing applications are used to illustrate the material, including topics such as audio and image processing.
Level of Study: Graduate
EECE 5520 Digital Image Processing (3 credits)
Theory and practice of image digitization, processing, coding and analysis. Representations of images, image models. Techniques of image enhancement and restoration. Image compaction and coding. Segmentation and image understanding. Students have the opportunity to experiment with several image processing techniques using the MATLAB Image Processing Toolbox.
Level of Study: Graduate
EECE 5530 Probability and Statistics for Engineers (3 credits)
Introduction to probability: probability space, random variables, distribution/density functions, expectation, correlation; transformation of random variables; elements of statistics: sample means, confidence intervals, survival rate, hypothesis testing, model parameter estimation; computational statistical analysis using Matlab; elements of stochastic processes: autocorrelation functions, power spectral density, wide-sense stationary processes, transmission through linear time-invariant systems; applications to engineering problems in circuits, reliability, failure analysis, data communication, computer networks, signal processing, and internet-traffic models.
Level of Study: Graduate
EECE 5550 Developments in Signal Processing (1-3 credits)
Course content is announced prior to each term. Students may enroll in the course more than once because subject matter changes. Depending upon the subject matter and the instructor, the class may be taught in traditional lecture format or as a seminar which focuses on readings from the current literature. Possible topics include filter design, DSP hardware, Nonlinear signal processing and multi-dimensional signal processing.
Level of Study: Graduate
EECE 5560 Introduction to Communication Systems (3 credits)
Survey of digital and analog communication systems including signal representation, modulation techniques, transmit and receive network design considerations.
Level of Study: Graduate
EECE 5565 Optical Fiber Communications (3 credits)
Fundamental principles and theories of optical fiber systems are introduced and developed. Review of electromagnetic principles of wave-guides. Step-Index and Graded-Index, single and multimode fibers. Signal analysis in optical fibers: mode interaction, attenuation, dispersion and pulse spreading. Operating characteristics of optical sources and photo-receivers with impact on system performance. Coupling to a fiber and distribution system. Optical fiber communication system design. Design Elective.
Level of Study: Graduate
EECE 5570 Wireless Communications (3 credits)
Fundamentals, analysis and design of cell systems, including trunking theory and grade of service. Large scale and small scale path loss analysis and modeling. Overview of modulation techniques, including amplitude and frequency modulating, and digital modulation techniques.
Level of Study: Graduate
EECE 5590 Developments in Communications (1-3 credits)
Course content announced prior to each term. Students may enroll in the course more than once as subject matter changes. May be taught in traditional lecture format or as a seminar which focuses on readings from current literature. Possible topics include digital modulation and detection, coding theory, information theory.
Level of Study: Graduate
EECE 5610 Object-Oriented Software Engineering (3 credits)
Presents advanced software engineering concepts in the context of object-oriented analysis and design. Topics include: concept of object-orientation, UML modeling techniques, use of CASE tools, use-case requirement analysis, modeling with classes, object-oriented design, design patterns, software quality, testing and correctness, sofware reuse and aspect-oriented software engineering.
Prerequisite: COEN 2610 or equiv.
Level of Study: Graduate
EECE 5620 Modern Programming Practices (3 credits)
Explores advanced topics in computer programming. Topics may include: design patterns, advanced graphical components, software component models such as Java Beans, the Java Security model, Java and databases, servlets, Java Server Pages, and Enterprise Java Beans.
Level of Study: Graduate
EECE 5630 Software Testing (3 credits)
Examines the relationship of software testing to quality, emphasizing testing techniques and the role of testing in the validation of system requirements. Topics include: module and unit testing, integration, walkthroughs and inspections, verification and validation, preventing and detecting errors, selecting and implementing project metrics, and defining test plans and strategies traced from system requirements.
Level of Study: Graduate
EECE 5650 Introduction to Algorithms (3 credits)
Introduction to the algorithms analysis. Topics to be covered include: the concepts of time and space complexity, advanced data structures, general issues in problem solving methodologies, greedy algorithms, dynamic programming, graph algorithms, AI-related algorithms, and an introduction to NP-completeness theory.
Prerequisite: COSC 2010 or equiv.
Level of Study: Graduate
EECE 5690 Developments in Computer Software (3 credits)
Course content announced prior to each term. Students may enroll in the course more than once as subject matter changes.
Prerequisite: Cons. of instr. Consent required.
Level of Study: Graduate
EECE 5710 Computer Hardware (3 credits)
Overview of computer system design. Cost and performance specification. Design of arithmetic and logic units. Fundamentals of central processor architecture and a comparative study of computer instruction set architectures. Detailed study of microprocessors, including instruction execution timing and other timing considerations. Discussions of memory and I/O devices, including the interfaces to the CPU and I/O transfer techniques. Study of common bus standards.
Level of Study: Graduate
EECE 5730 Computer Architecture (3 credits)
Review of basic computer architecture. Evaluation of architecture performance. Design and evaluation of instruction sets. Pipeline processors and instruction scheduling. Vector processors. Memory hierarchy and design including cache, main and virtual memories. Memory protection schemes. Input/output and its relation to system performance.
Level of Study: Graduate
EECE 5740 Advanced VHDL and FPGA Design (3 credits)
Presents the background, abstractions, and techniques for advanced digital circuits design and optimization. Emphasizes specification and synthesis using VHDL and prototyping using FPGAs of complex systems. Such systems represent examples from various application domains, including processors, image and video processing, filtering and other DSPs, and power electronics.
Level of Study: Graduate
EECE 5790 Developments in Computer Hardware (3 credits)
Course content announced prior to each term. Students may enroll in the course more than once as subject matter changes.
Prerequisite: Cons. of instr.
Level of Study: Graduate
EECE 5800 Networks and Security (3 credits)
A variety of relevant topics are discussed, including communication network architecture, networking protocols, error control, media access control, routing, addressing, congestion/flow control, TCP and UDP, cryptography, authentication and VPNs.
Level of Study: Graduate
EECE 5820 Operating Systems and Networking (3 credits)
Introduces the fundamental concepts of operating systems together with the basics of networking and communications including: memory management, scheduling, concurrent processing, device management, file systems, networking, security, and system performance. Examples are drawn from legacy and modern operating systems.
Level of Study: Graduate
EECE 5830 Introduction to Computer Graphics (3 credits)
Introduction to computer graphics algorithm design and implementation; includes considerable actual computer graphics experience. Topics include: point-plotting and line-drawing techniques, two-dimensional curve fitting, two-and three-dimensional graphics, clipping, windowing, hidden line removal, modeling, lighting and shading, 3D viewing, texturing, shadowing, introduction to ray tracing, input-output devices, and other topics as future trends dictate.
Level of Study: Graduate
EECE 5840 Computer Security (3 credits)
Introduction to the important issues in computer security, including cryptography, program security, operating system security, database security, and network security. Also discusses the legal, ethical and privacy issues that arise in computer security. Programming projects enable the student to practice implementing many of the security measures discussed in class.
Level of Study: Graduate
EECE 5850 Introduction to Intelligent Systems (3 credits)
Provides a broad exposure to intelligent systems, including related fields such as artificial and computational intelligence. Topics include: intelligent agents, search, game playing, propositional logic and first-order predicate calculus, uncertainty, learning, communication and perception, and philosophical foundations of intelligent systems.
Prerequisite: COSC 2010, MATH 1450, MATH 2105 or equiv.
Level of Study: Graduate
EECE 5860 Introduction to Neural Networks and Fuzzy Systems (3 credits)
Concepts of artificial neural network architectures and training algorithms, supervised and unsupervised learning, linear and non-linear neural networks, feedback neural networks, applications in scientific and engineering areas, fundamentals of fuzzy sets and fuzzy logic, fuzzy rules and inference systems, fuzzy pattern classification and clustering analysis and fuzzy control systems.
Prerequisite: COSC 2010 and MATH 1451 or equiv.
Level of Study: Graduate
EECE 5870 Evolutionary Computation (3 credits)
Covers a set of search methods based on the Darwinian principle of survival of the fittest. The methods include genetic algorithms, evolutionary strategies and evolutionary and genetic programming, which have been successfully applied to many different problem domains including optimization, learning, control, and scheduling. Provides students with the background and knowledge to implement various evolutionary computation algorithms, discusses trade-offs between different evolutionary algorithms and other search methods, and discusses issues related to the application and performance evaluation of evolutionary algorithms.
Prerequisite: COSC 2010, MATH 1450, MATH 2105 or equiv.
Level of Study: Graduate
EECE 5890 Developments in Intelligent Systems (1-3 credits)
Course content is announced prior to each term. Students may enroll in the course more than once because subject matter changes. Depending upon the subject matter and the instructor, the class may be taught in traditional lecture format or as a seminar which focuses on readings from the current literature.
Level of Study: Graduate
EECE 6010 Advanced Engineering Mathematics (3 credits)
Linear algebra and matrix theory, ordinary differential equations and complex variables emphasizing both theoretical and numerical aspects as well as engineering applications.
Prerequisite: MATH 2451 or equiv.
Level of Study: Graduate
EECE 6020 Probability and Random Processes in Engineering (3 credits)
Probability, random variables, statistics, and random processes, emphasizing both theoretical and numerical aspects as well as engineering applications.
Prerequisite: MATH 2451 or equiv.
Level of Study: Graduate
EECE 6090 Advanced Engineering 1 (3 credits)
Mathematics, image processing, signal processing, image reconstruction, and imaging systems in medical imaging applications. Offered fall term at the General Electric Medical Systems facility. This course extends beyond the Marquette term; students receive an IC grade initially. The IC will be changed to an A-F grade at the end of the course.
Prerequisite: Cons. of instr.; GE employee. Consent required.
Level of Study: Graduate
EECE 6092 Advanced Engineering 2 (3 credits)
Problem solving methodology, software engineering tools and environment (typical topics: UNIX, C, data structures, object oriented paradigm, programming strategies), and hardware engineering tools (typical topics: analog and digital CAD, PALs, VME, applications). EECE 6092 and EECE 6810 may not both be used to meet degree requirements. Offered spring term at the General Electric Medical Systems facility. This course extends beyond the Marquette term; students receive an IC grade initially. The IC will be changed to an A-F grade at the end of the course.
Prerequisite: Cons. of instr.; GE employee. Consent required.
Level of Study: Graduate
EECE 6094 Advanced Engineering 3 (3 credits)
Covers advanced concepts in medical imaging and systems. Offered spring term at the General Electric Medical Systems facility. This course extends beyond the Marquette term; students receive an IC grade initially. The IC will be changed to an A-F grade at the end of the course.
Prerequisite: Cons. of instr.; GE employee. Consent required.
Level of Study: Graduate
EECE 6110 Advanced Electromagnetic Fields (3 credits)
Solutions of Laplace and Poisson equations arising from electro and magneto static field configurations. Separation of variables, numerical relaxation, and conformal mapping techniques.
Prerequisite: EECE 3110 or equiv.
Level of Study: Graduate
EECE 6120 Electromagnetic Theory (3 credits)
Review of Maxwell's equations and waves in dielectric and lossy media; image theory, induction theorem and Green's function. Plane, cylindrical and spherical wave functions; radiation and antennas; rectangular, cylindrical waveguides and cavities; spherical cavities. Perturbation and variation techniques and moment techniques.
Prerequisite: EECE 3120 or equiv.
Level of Study: Graduate
EECE 6130 Numerical Techniques in Electromagnetics (3 credits)
Introduction and overview of numerical methods in electromagnetics, focusing on high frequency methods. Topics covered include: a review of analytic methods and the generalized multipole technique, finite difference methods, variational techniques, and the solution to integral equations via the method of moments.
Level of Study: Graduate
EECE 6210 Advanced Electric Machines and Drives (3 credits)
Machine characterization. Development and application of transformation theory to synchronous and induction machines to predict machine performance under steady state and abnormal conditions. Modeling of permanent magnet and switched reluctance machines, as well as other advanced machine systems. Dynamic performance prediction of electric machines and associated power electronics using equivalent network models and computer simulations.
Level of Study: Graduate
EECE 6220 Advanced Concepts in the Design and Modeling of Electric Machines and Drives (3 credits)
Presents advanced concepts and methodologies in designing and modeling modern electric machines controlled and operated from electronically switched electric drives. Involves methods of analysis and computation of the adverse synergistic effects which occur between the space harmonics generated in electric machinery due to magnetic circuit topologies, time harmonics generated by electronic switching in the controllers/drives, and the impact of this synergism on losses, efficiency, torque quality and other performance issues. Includes full and rigorous analysis and inclusion of such space harmonics, and time harmonics. Studies, in detail, methods of mitigation or elimination of these effects using advance modeling concepts and tools.
Prerequisite: ELEN 3210 or equiv.
Level of Study: Graduate
EECE 6230 Finite Element Analysis (3 credits)
Basic field formulations using Maxwell's electromagnetic field equations. General definitions and formulations of finite element discretization. Consideration of applications and method implementation. Application of the finite element method to engineering and design problems. Post processing, practical aspects and other considerations. Application of method involves the use of commercially available software packages as well as computer code developed during this course.
Prerequisite: MATH 2451 or equiv.; and proficiency in computer programming.
Level of Study: Graduate
EECE 6240 Renewable Energy: Policy, Technology and Sustainability (3 credits)
Focuses on the legal policy, technical framework and sustainability for electrical energy project development in Wisconsin. Legal policy topics include corporate structuring, financing, taxation, permitting and strategies to support and oppose such projects. Describes a general overview of technical considerations for emerging renewable technologies in Wisconsin, such as biomass, waste to energy, geothermal, solar and wind projects. Introduces sustainable development aspects of renewable energy projects to identify impacts to the triple bottom line (social, environmental and financial). Fosters a collaborative approach in a multidisciplinary setting to developing renewable energy projects, consistent with legal and technical partnerships that occur in practice.
Level of Study: Graduate
EECE 6310 Modern Control Theory (3 credits)
Review of linear algebra and matrices. State variable analysis of continuous-time and discrete-time systems. Controllability and observability of linear systems. Stability of linear and nonlinear systems. Design of feedback control systems. Introduction to optimal control theory.
Prerequisite: EECE 6010 which may be taken concurrently; or MEEN 6101 which may be taken concurrently.
Level of Study: Graduate
EECE 6320 Optimal Control (3 credits)
Presents an in-depth understanding of the problems in optimal control theory and their applications. Presents calculus of variations, linear quadratic regulator design, dynamic programming, time-optimal, and output feedback regulating and tracking optimal control techniques for continuous-time systems. Presents discrete-time techniques for calculus of variations, linear quadratic tracking, output feedback optimal control, and time-optimal control. Also presents optimal observers.
Level of Study: Graduate
EECE 6330 Nonlinear and Adaptive Control (3 credits)
Review of state space and modern control theory with emphasis on stability, convergence, and robustness. Lyapunov stability and advanced stability theory. Nonlinear systems analysis, feedback linearization, and sliding mode control. Real-time parameter identification and adaptive observers. Continuous-time and discrete-time self-tuning regulators and model-reference adaptive systems. Introduction to system identification and adaptive control using artificial neural networks.
Level of Study: Graduate
EECE 6340 Stochastic Systems Estimation and Control (3 credits)
Modeling probabilistic dynamical behavior with stochastic systems. Analysis of behavior of linear continuous and discrete time systems via simulation and analytical methods. Filter construction for state and parameter estimation using noisy and incomplete measurements for linear and nonlinear systems and measurements models. Design of optimal controllers based on quadratic criteria for linear stochastic systems. Consent required.
Level of Study: Graduate
EECE 6420 Infrared and Photonics Sensors: Theory and Applications (3 credits)
Fundamentals of infrared (IR) technology. Performance capabilities and operational limitations of IR and photonic devices and sensors. Principles and theory of IR radiation. Analysis of transmission characteristics of optical signals through the atmosphere: effects of scattering, absorption and diffraction as a function of atmospheric parameters. IR sources and detectors. Passive and active IR devices, components and sensors. IR-based and photonic-based sensors; applications to environmental sensing, biotechnology and medical analysis, space and surveillance systems. IR signature analysis. Aspects of advanced IR and photonic technologies with possible performance improvements.
Prerequisite: ELEN 3110 or equiv.
Level of Study: Graduate
EECE 6425 Introduction to Microelectromechanical Systems (MEMS) (3 credits)
Covers the history, design and fabrication of microelectromechanical systems (MEMS) and the basic operating theory of selected MEMS transducers. Typical fabrication methods covered include surface micromachining, bulk micromachining and micro-molding. Surveys a broad range of MEMS transducers (sensors and/or actuators) and applications. Activities include a weekly computer laboratory where students design, layout and simulate classical MEMS devices, to include electrostatic actuators, electro-thermal actuators, multilayer misfit-strain actuators, comb-drive resonators and hinged structures. Students prepare several MEMS designs using a computer-aided drawing program as if to be submitted for fabrication to a commercial micromachining process.
Prerequisite: EECE 4410 or cons. of instr.
Level of Study: Graduate
EECE 6430 Microelectromechanical Systems and Sensors (3 credits)
Overview of microelectromechanical-MEMS-transducers and sensors. Basic engineering sciences and fundamental principles relevant to mechanical sensors and micromachined mechanical transducers. Mathematical models and design of microelectromechanical systems. Microfabrication techniques, materials and processes. Mechanical transduction techniques, pressure sensors, force and torque sensors, inertial sensors, flow sensors, micromachined resonant sensors, micromachined chemical sensors.
Prerequisite: ELEN 3110 or equiv.
Level of Study: Graduate
EECE 6450 Surface-Acoustic-Wave Devices (3 credits)
Theory of surface and other acoustic modes; design, analysis, and performance of interdigital devices; multistrip couplers; SAW resonators; dispersive delay lines; system applications; current research areas.
Level of Study: Graduate
EECE 6510 Optimal and Adaptive Digital Signal Processing (3 credits)
Introduction to optimal and adaptive signal processing theory and applications. Topics include: statistically optimal gradient descent methods, such as least-mean-squares and minimal error methods, least squares and recursive least squares, Wiener filters, linear prediction, Kalman filters and performance and convergence analysis techniques.
Level of Study: Graduate
EECE 6520 Digital Processing of Speech Signals (3 credits)
Introduction to the fundamentals of speech processing, including speech production and perception models and frequency-domain analysis methods such as, linear predictive coding and cepstral analysis. Applications studied include: speech coding, enhancement, recognition and synthesis.
Prerequisite: EECE 5510 or equiv.
Level of Study: Graduate
EECE 6530 Chaos and Nonlinear Signal Processing (3 credits)
Introduces the theory and practice for analyzing chaotic and nonlinear signals. Examines methods for finding hidden structures in signals and time series, using techniques such as phase space reconstruction. Discusses topics previously mentioned along with machine learning, time series analysis, adaptive signal processing, wavelets and nonlinear dynamics.
Prerequisite: EECE 5510 or equiv.
Level of Study: Graduate
EECE 6540 Advanced Digital Image Processing (3 credits)
Theory and practice of image digitization, processing, coding and analysis. Representations of images, image models. Techniques of image enhancement and restoration. Image compaction and coding. Segmentation and image understanding.
Level of Study: Graduate
EECE 6560 Information and Coding Theory (3 credits)
Introduction to information measure, mutual information, self-information, entropy, encoding of information, discrete and continuous channels, channel capacity, error detection, error correcting codes, group codes, cyclic codes, BCH codes, convolution codes, and advanced codes.
Level of Study: Graduate
EECE 6570 Detection, Estimation and Learning Theory (3 credits)
Topics include hypothesis testing: Bayes, minimax and Neaman-Pearson detection, asymptotic relative efficiency; Karhunen-Loeve representation; optimal discrete-, continuous-time and sequential detection: structure and performance; maximum likelihood, minimum mean square-error, and maximum a posteriori estimation; sufficient statistics; generalized bounds on estimator performance; linear estimation; Kalman-Bucy and extended Kalman filtering; importance sampling; blind estimation; and elements of compressive sensing.
Prerequisite: EECE 6020 or equiv.
Level of Study: Graduate
EECE 6710 Computer Architecture (3 credits)
Review of basic computer architecture. Evaluation of architecture performance. Design and evaluation of instruction sets. Pipeline processors and instruction scheduling. Vector processors. Memory hierarchy and design, including cache, main and virtual memories. Memory protection schemes. Input/output and its relation to system performance.
Prerequisite: COEN 5710 or equiv.
Level of Study: Graduate
EECE 6810 Algorithm Analysis and Applications (3 credits)
Introduction to the analysis of algorithms. Topics include: asymptotic complexity notation, recursion analysis, basic and advanced data structures, sorting methodologies, dynamic programming, and graph algorithms, including heuristic search techniques such as best-first and A-star algorithms. Advanced topics include NP-completeness theory and linear programming.
Prerequisite: EECE 5650.
Level of Study: Graduate
EECE 6820 Artificial Intelligence (3 credits)
Provides a comprehensive survey of artificial intelligence. Topics include: search, logic, planning, uncertainty, learning, communication and perception, robotics and philosophical foundations of artificial intelligence.
Prerequisite: COSC 2010, MATH 1450, MATH 2105 or equiv.
Level of Study: Graduate
EECE 6822 Machine Learning (3 credits)
An introduction to a range of adaptive computer algorithms that learn models from data. Explores the theoretical foundations of machine learning, including computational learning theory and PAC learnability. Examples of machine learning algorithms studied include: decision trees, artificial neural networks, Bayesian learners, evolutionary algorithms and ensemble techniques.
Prerequisite: EECE 6820 or equiv.
Level of Study: Graduate
EECE 6830 Pattern Recognition (3 credits)
Theory and application of statistical pattern recognition, hypothesis testing and parameter estimation. Topics include: probability distribution models, Bayesian decision theory and hypothesis testing, classical and modern approaches to parameter estimation, parametric and non-parametric classifiers. Also, covered are feature selection and transformation techniques such as Principal Components Analysis, a wide range of classifier models and supervised and unsupervised clustering.
Prerequisite: EECE 6020 or equiv.
Level of Study: Graduate
EECE 6840 Neural Networks and Neural Computing (3 credits)
Advanced concepts of artificial neural networks and neural computing. Mathematical modeling of neural network architectures including feed-forward and recurrent neural network models. Optimization algorithms in the neural network training. Kohonen Feature Maps (KFM), Learning Vector Quantization (LVQ) and Support Vector Machine (SVM) models. Applications include: optimization, pattern recognition and intelligent controls.
Prerequisite: MATH 1451 or equiv.
Level of Study: Graduate
EECE 6931 Topics in Electrical and Computer Engineering (1-5 credits)
Course content announced prior to each offering. Students may enroll more than once as subject matter changes. Possible topics include computer operating systems, multiprogramming and multi-processing systems, computer architecture, optimal and adaptive control, stochastic control, estimation theory, and nonlinear analysis.
Level of Study: Graduate
EECE 6932 Advanced Topics in Electrical and Computer Engineering (3 credits)
Course content announced prior to each offering. Students may enroll more than once as subject matter changes. Possible topics include: computer operating systems, multiprogramming and multi-processing systems, computer architecture, optimal and adaptive control, stochastic control, estimation theory, and nonlinear analysis. Consent required.
Level of Study: Graduate
EECE 6952 Department Colloquium (0 credits)
Scholarly presentations on current topics in electrical engineering and computer engineering and related areas by visiting and resident investigators. Required of all full-time graduate students each term. Required of all full-time EECE graduate students. S/U grade assessment.
Level of Study: Graduate
EECE 6953 Seminar in Electrical and Computer Engineering (0-3 credits)
0 credit will be SNC/UNC grade assessment; 1-3 credits will be graded.
Prerequisite: Cons. of instr. Consent required.
Level of Study: Graduate
EECE 6964 Practicum for Research and Development in Computing (3 credits)
Provides students, who are enrolled in the M.S. in computing program, an opportunity to participate in the practice of research and/or development in the area of computing. Course Guidelines are available from EECE and MSCS Departments. Available only to full-time students. At most, six credits of EECE 6964 OR MSCS 6964 may be counted toward graduation. S/U grade assessment.
Prerequisite: 3.00 MU GPA; must be enrolled in Plan B option of the M.S. in computing program and have completed at least 21 credit hours of course work, with 15 credit hours earned in graduate (6000-level) courses.
Level of Study: Graduate
EECE 6995 Independent Study in Electrical and Computer Engineering (1-5 credits)
Graduate independent study project of either a theoretical or experimental nature.
Prerequisite: Cons. of instr. and cons. of dept. ch. Consent required.
Level of Study: Graduate
EECE 6999 Master's Thesis (1-6 credits)
S/U grade assessment.
Prerequisite: Cons. of instr. Consent required.
Level of Study: Graduate
EECE 8097 Career Discernment/Career Diversity Skills (0 credits)
Provides students with an understanding of the career realities of their discipline and to offer them the opportunity to discern their career values and how to obtain the skills and experiences that will match that valued career direction. S/U grade assessment.
Prerequisite: Enrolled in the EECE Ph.D. program.
Level of Study: Graduate
EECE 8098 Communication Skills (0 credits)
Provides students with the ability to effectively communicate and lead discussions across various academic and non-academic audiences within various contexts, formats and media. S/U grade assessment.
Prerequisite: Enrolled in the EECE Ph.D. program. Consent required.
Level of Study: Graduate
EECE 8099 Diversity, Equity and Inclusion Skills (0 credits)
Provides students with foundational understandings of diversity, equity, and inclusion (DEI) along with practical tools to engage across difference. Discusses how, in today’s interconnected world, understanding DEI is essential for professional growth, effective communication, fostering inclusive communities and reimagining our physical and social realities for greater accessibility. Designed to equip students with knowledge, skills and perspectives necessary to examine how they might engage in more equitable and inclusive practices in their future careers. S/U grade assessment.
Prerequisite: Enrolled in the EECE Ph.D. program.
Level of Study: Graduate
EECE 8932 Advanced Topics in Electrical and Computer Engineering (3 credits)
Course content announced prior to each offering. Students may enroll more than once as subject matter changes. Possible topics include: computer operating systems, multiprogramming and multi-processing systems, computer architecture, optimal and adaptive control, stochastic control, estimation theory, and nonlinear analysis.
Prerequisite: Cons. of instr. Consent required.
Level of Study: Graduate
EECE 8986 Cooperative Education in Electrical and Computer Engineering (0-1 credits)
Offers an additional educational experience for graduate students in electrical and computer engineering, intended to increase student professional development and growth as an independent engineer and/or researcher. Provides the opportunity to work on-site with engineers from industry.
Prerequisite: Cons. of dept. ch. Consent required.
Level of Study: Graduate
EECE 8995 Independent Study in Electrical and Computer Engineering (1-5 credits)
Graduate independent study project of either a theoretical or experimental nature.
Prerequisite: Cons. of instr. and cons. of dept. ch. Consent required.
Level of Study: Graduate
EECE 8999 Doctoral Dissertation (1-12 credits)
S/U grade assessment.
Prerequisite: Cons. of instr. Consent required.
Level of Study: Graduate
EECE 9970 Graduate Standing Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Designated as less than half-time status only, cannot be used in conjunction with other courses, and does not qualify students for financial aid or loan deferment.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9974 Graduate Fellowship: Full-Time (0 credits)
Fee. S/U grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9975 Graduate Assistant Teaching: Full-Time (0 credits)
Fee. S/U grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9976 Graduate Assistant Research: Full-Time (0 credits)
Fee. S/U grade assessment. Designated as full-time status. If a student is already registered in other courses full time, this continuation course is not needed.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9986 Master's Comprehensive Examination Preparation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week toward their master's comprehensive exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9987 Doctoral Qualifying Examination Preparation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week toward their doctoral qualifying exam.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9988 Doctoral Qualifying Examination Preparation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week toward their doctoral qualifying exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9989 Doctoral Qualifying Examination Preparation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week toward their doctoral qualifying exam. May be taken in conjunction with credit-bearing or other non-credit courses to result in the status indicated, as deemed appropriate by the department.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9994 Master's Thesis Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9995 Master's Thesis Continuation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9996 Master's Thesis Continuation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their master's thesis. All six thesis credits required for the degree should be completed before registering for non-credit Master's Thesis Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9997 Doctoral Dissertation Continuation: Less than Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of less than half-time status. Requires that the student is working less than 12 hours per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9998 Doctoral Dissertation Continuation: Half-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of half-time status. Requires that the student is working more than 12 to less than 20 hours per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation.
Prerequisite: Consent required.
Level of Study: Graduate
EECE 9999 Doctoral Dissertation Continuation: Full-Time (0 credits)
Fee. S/U grade assessment. Allows a student to be considered the equivalent of full-time status. Requires that the student is working 20 hours or more per week on their doctoral dissertation. All 12 dissertation credits required for the degree should be completed before registering for non-credit Doctoral Dissertation Continuation.
Prerequisite: Consent required.
Level of Study: Graduate