Duluth campus
 
Duluth Campus

Computer Science M.S.

Computer Science
Swenson College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Department of Computer Science, University of Minnesota Duluth, 1114 Kirby Drive, 320 Heller Hall, Duluth, MN 55812 (218-726-7607; fax: 218-726-8240)
Email: cs@d.umn.edu
  • Program Type: Master's
  • Requirements for this program are current for Fall 2022
  • Length of program in credits: 30 to 32
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
Computer science is a discipline that involves understanding the design of computers and computational processes. Study in the field ranges from the theoretical study of algorithms to the design and implementation of software at the systems and applications levels. The Master of Science is a 2-year program that provides the necessary foundational studies for graduates planning to pursue either a doctorate in computer science or a career as a computer scientist in business or industry.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
The program is designed for students with undergraduate degrees in computer science or a related field.
Other requirements to be completed before admission:
Students with undergraduate degrees in fields other than computer science or related areas may be considered for admittance if they have completed the following courses or their equivalents: CS 1511-1521 - Computer Science I-II; CS 2511 - Software Analysis and Design; CS 2521 - Computer Organization and Architecture; MATH 3355 - Discrete Mathematics or CS 2531 - Discrete Structures for Computer Science' CS 3531 Automata & Formal Languages; and at least three of CS 4312 - Operating Systems, CS 4332 - Computer Security, CS 4422 - Computer Networks, CS 4122 - Advanced Data Structures and Algorithms, CS 4212 - Computer Graphics, CS 4322 - Database Management Systems. The appropriate math prerequisites, namely MATH 1296 - Calculus I and STAT 3611 - Introduction to Probability and Statistics, are also required.
Special Application Requirements:
Admission is for fall semester only. International and domestic applicants whose first language is not English must submit current score(s) from one of the following tests:
Applicants must submit their test score(s) from the following:
  • GRE
International applicants must submit score(s) from one of the following tests:
  • IELTS
    • Total Score: 6.5
    • Reading Score: 6.5
    • Writing Score: 6.5
  • MELAB
    • Final score: 80
Key to test abbreviations (GRE, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan A: Plan A requires 20 major credits, 0 credits outside the major, and 10 thesis credits. The final exam is oral.
Plan B: Plan B requires 32 major credits and 0 credits outside the major. The final exam is oral. A capstone project is required.
Capstone Project:The Plan B project, completed in consultation with the advisor, comprises significant programming research. The project is often based on an extended 5XXX-level course assignment.
This program may not be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
Computer Science Coursework (12 credits)
Select 12 credits from the following in consultation with the advisor:
CS 5112 - Advanced Theory of Computation (4.0 cr)
CS 5122 - Advanced Algorithms and Data Structures (4.0 cr)
CS 5212 - Computer Graphics (4.0 cr)
CS 5222 - Artificial Intelligence (4.0 cr)
CS 5232 - Introduction to Machine Learning and Data Mining (4.0 cr)
CS 5242 - Natural Language Processing (4.0 cr)
CS 5312 - Operating Systems (4.0 cr)
CS 5322 - Database Management Systems (4.0 cr)
CS 5332 - Computer Security (4.0 cr)
CS 5342 - Compiler Design (4.0 cr)
CS 5412 - Computer Architecture (4.0 cr)
CS 5422 - Computer Networks (4.0 cr)
CS 5432 - Sensors and Internet of Things (4.0 cr)
CS 5642 - Advanced Natural Language Processing (4.0 cr)
CS 5732 - Advanced Computer Security (4.0 cr)
Graduate Seminar (2 credits)
Take 1 credit the first fall semester, and 1 credit the second fall semester of the following:
CS 8993 - Seminar (1.0 cr)
Electives (6 credits)
Select 6 credits from the following in consultation with the advisor. Other 5xxx-level or higher coursework can be chosen with approval of the advisor and director of graduate studies.
CHE 5011 - Process Optimization: Lean Six Sigma (3.0 cr)
CS 5732 - Advanced Computer Security (4.0 cr)
CS 5995 - Special Topics: (Various Titles to be Assigned) (1.0-4.0 cr)
EDSE 5000 - Introduction to Post-Secondary Teaching (2.0 cr)
EDUC 5413 - Teaching With Technology (4.0 cr)
EDUC 7002 - Diversity and Social Justice (3.0 cr)
EE 5151 - Digital Control System Design (3.0 cr)
EE 5161 - Linear State-Space Control Systems (3.0 cr)
EE 5311 - Design of VLSI Circuits (4.0 cr)
EE 5315 - Multiprocessor-Based System Design (3.0 cr)
EE 5351 - Introduction to Robotics and Mobile Robot Control Architectures (3.0 cr)
EE 5477 - Antennas and Transmission Lines (3.0 cr)
EE 5479 - Antennas and Transmission Lines Laboratory (1.0 cr)
EE 5501 - Energy Conversion System (3.0 cr)
EE 5522 - Power Electronics I (3.0 cr)
EE 5533 - Grid- Resiliency, Efficiency and Technology (3.0 cr)
EE 5621 - Microelectronics Technology (3.0 cr)
EE 5741 - Digital Signal Processing (3.0 cr)
EE 5745 - Medical Imaging (3.0 cr)
EE 5765 - Modern Communication (4.0 cr)
EE 5801 - Introduction to Artificial Neural Networks (3.0 cr)
EE 8151 - Optimal Control Systems (3.0 cr)
EE 8741 - Digital Image Processing (4.0 cr)
EE 8765 - Digital Communications (3.0 cr)
ENGL 5802 - English Language for Educators (4.0 cr)
MATH 5201 - Real Variables (4.0 cr)
MATH 5202 - Applied Functional Analysis (3.0 cr)
MATH 5233 - Mathematical Foundations of Bioinformatics (3.0 cr)
MATH 5260 - Dynamical Systems (3.0 cr)
MATH 5270 - Modeling with Dynamical Systems (3.0 cr)
MATH 5280 - Partial Differential Equations (3.0 cr)
MATH 5327 - Advanced Linear Algebra (3.0 cr)
MATH 5330 - Theory of Numbers (3.0 cr)
MATH 5347 - Applied Algebra and Cryptology (3.0 cr)
MATH 5365 - Graph Theory (3.0 cr)
MATH 5366 - Enumerative Combinatorics (3.0 cr)
MATH 5371 - Abstract Algebra I (3.0 cr)
MATH 5372 - Abstract Algebra II (3.0 cr)
MATH 5810 - Linear Programming (3.0 cr)
MATH 5830 - Numerical Analysis: Approximation and Quadrature (4.0 cr)
MATH 5840 - Numerical Analysis: Systems and Optimization (4.0 cr)
MATH 5850 - Numerical Differential Equations (4.0 cr)
MATH 8201 - Real Analysis (3.0 cr)
MIS 5241 - Data Analytics for Managerial Decision Making (3.0 cr)
PHYS 5053 - Data Analysis Methods in Physics (3.0 cr)
PSY 5621 - Cognition and Emotion (3.0 cr)
STAT 5411 - Analysis of Variance (3.0 cr)
STAT 5511 - Regression Analysis (3.0 cr)
STAT 5515 - Multivariate Statistics (3.0 cr)
STAT 5521 - Applied Time Series Analysis (3.0 cr)
STAT 5531 - Probability Models (4.0 cr)
STAT 5571 - Probability (4.0 cr)
STAT 5572 - Statistical Inference (4.0 cr)
STAT 8611 - Linear Models (3.0 cr)
Plan Options
Plan A
Thesis Credits
Take 10 master's thesis credits.
CS 8777 - Thesis Credits: Master's (1.0-24.0 cr)
-OR-
Plan B
Plan B Project (4 credits)
Take 4 credits of the following in consultation with the advisor:
CS 8794 - Project Credits: Master's (1.0-4.0 cr)
Additional Coursework (8 credits)
Select 8 credits from the lists above in consultation with the advisor to complete the 32-credit requirement. Other courses can be chosen with advisor approval.
 
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CS 5112 - Advanced Theory of Computation
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Mathematical theory of computation and complexity. Deterministic and nondeterministic Turing machines, Church-Turing Thesis, recursive and recursively enumerable languages. Lambda calculus. Undecidable problems, Rice's Theorem, undecidability of first-order logic and Gödels incompleteness theorem. Time and space complexity, reducibility, completeness for complexity classes, Cook's Theorem, P versus NP, Savitch's Theorem, complexity hierarchy. pre-req: Grad student, CS 3512 or 3531 or instructor consent
CS 5122 - Advanced Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Survey of advanced data structures and algorithms such as heaps and heapsort, quicksort, red-black trees, B-tress, hash tables, graph algorithms, divide and conquer algorithms, dynamic programming, and greedy algorithms. Methods for proving correctness and asymptotic analysis. pre-req: grad student; CS 2511, 2531 or 3512 or MATH 3355 or instructor consent; a grade of C- or better in all prerequisite courses
CS 5212 - Computer Graphics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduces the fundamentals of computer graphics to create 2D images from 3D data representations, the graphics pipeline, 3D representations of objects such as triangles and triangle meshes, surface material representations, color representation, vector and matrix mathematics, 3D coordinates and transformations, transport of light energy, global illumination, graphics rendering systemes, ray tracing, rasterization, real-time rendering, OpenGL and computer graphics hardware. prereq: graduate student, CS 2511, (2531 or 3512 or MATH 3355), (MATH 3280 or 3326) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5222 - Artificial Intelligence
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Principles and programming methods of artificial intelligence. Knowledge representation methods, state space search strategies, and use of logic for problem solving. Applications chosen from among expert systems, planning, natural language understanding, uncertainty reasoning, machine learning, and robotics. Lectures and labs will utilize suitable high-level languages (e.g., Python or Lisp). prereq: grad student, 2511, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5232 - Introduction to Machine Learning and Data Mining
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to primary approaches to machine learning and data mining. Methods selected from decision trees, neural networks, statistical learning, genetic algorithms, support vector machines, ensemble methods, and reinforcement learning. Theoretical concepts associated with learning, such as inductive bias and Occam's razor. This is a potential Master's project course. prereq: grad student, 2511, 2531 or 3512 or MATH 3355, Stat 3611 or 3411, Math 3280 or 3326 or instructor consent; a grade of C- or better is required in all prerequisite courses
CS 5242 - Natural Language Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Techniques for creating computer programs that analyze, generate, and understand written human language. Emphasizes broad coverage of both rule-based and empirical data-driven methods. Topics include word-level approaches, syntactic analysis, and semantic interpretation. Applications selected from conversational agents, sentiment analysis, information extraction, and question answering. Significant research project that includes experimental results, written report, and clear grasp of ethical considerations involved. prereq: CS 2511, (2531 or 3512 or MATH 3355), grad student or instructor consent; a grade of C- or better is required in the prerequisite course; credit will not be granted if already received for CS 4242 or 5761
CS 5312 - Operating Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Operating system as resource manager. Modern solutions to issues such as processor management and scheduling, concurrency and related problems including deadlocks, memory management and protection, file system design, virtualization, distributed and cloud computing. Concepts including concurrency are illustrated via laboratory assignments, This is a potential Master's project course. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5322 - Database Management Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Entropy and the underlying characteristics of text. Encryption-basic techniques based on confusion and diffusion and modern day encryption. Access, information flow and inference control. Program threats and intrusion detection/prevention. Network and Internet security. Firewalls, trusted systems, network authentication. Privacy and related social issues. Planning, Incidents, and Recovery. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent; a grade of C- or better is required in all prerequisite courses
CS 5332 - Computer Security
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Entropy and the underlying characteristics of text. Encryption-basic techniques based on confusion and diffusion and modern day encryption. Access, information flow and inference control. Program threats and intrusion detection/prevention. Network and Internet security. Firewalls, trusted systems, network authentication. Privacy and related social issues. Planning, Incidents, and Recovery. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent; a grade of C- or better is required in all prerequisite courses
CS 5342 - Compiler Design
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
A selection from the following topics: finite-state grammars, lexical analysis, and implementation of symbol tables. Context-free languages and parsing techniques. Syntax-directed translation. Run-time storage allocation. Intermediate languages. Code generation methods. Local and global optimization techniques. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5412 - Computer Architecture
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Broad coverage of computer architecture, with a focus on the development of the stored program computer and the historical evolution of architectures. Includes coverage of significant architectures based on vacuum tubes, transistors, and integrated circuits. Impact of Moore?s Law and possible paradigms for the future including quantum and molecular computing. prereq: 2521, (2531 or 3512 or MATH 3355), grad student or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5422 - Computer Networks
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Introduction to computer networking, network programming, networking hardware and associated network protocols. Layered network architecture, network services, and implementation of computer networking software. prereq: grad student, 2511, 2521, (2531 or 3512 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses
CS 5432 - Sensors and Internet of Things
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course will introduce a broad range of sensors such as wearable biosensors that measure physiological changes, psychological changes, brain electrical activity, muscle impedance, and other sensors such as kinematic sensors, virtual reality, motion capture, luminosity and a range of robots, varying in size, features and autonomous capabilities, while emphasizing the basic principles of sensing for temperature, motion, sound, light, position, displacement, etc. IoT are ubiquitous systems that are built using embedded processors, sensors, other electronics and communication mechanisms. You will be introduced to IoTs through lectures, hands-on labs, and research papers. You will interface (embedded programming) various sensors with an AI based System-on-a-Chip (SoC) to learn to design a complete IoT system. In addition, you will learn to identify and mitigate any ethical issues related to this topic. Students will also learn the latest advances in the field of sensors and IoTs. pre-req: grad student, CS 2511, CS 2521, (CS 2531 or MATH 3355) or instructor consent, a grade of C- or better is required in all prerequisite courses, no credit if CS 4432 taken
CS 5642 - Advanced Natural Language Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Advanced techniques for creating computer programs that analyze, generage, and understand written human language. Emphasizes current empirical data-driven methods. Topics include sentence level representations, vector semantics, and models of document understanding. Applications selected from word sense discovery, machine translation, sentiment and option mining, and social computing. Significant research project that includes experimental results, written report, and clear grasp of ethical considerations involved. pre-req: CS 4242 or 5242, grad student or instructor consent; a grade of C- or better is required in the prerequisite course.
CS 5732 - Advanced Computer Security
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Broad, active, hands-on and implementation-based approach to computer security. Fundamental cryptographic theory, advanced techniques and application. Complexity, cryptanalysis, and impact of technological change. Core security theory; confidentiality, integrity, availability. Security models. Risk assessment and decision-making. Issues for general -purpose, trusted and cloud operating system security including hardware requirements, authentication, access control, information flow and assurance. Program and network security fundamentals and best practices including coding principles, firewalls and network design. Exploits, defenses and remediation for multiple issues pertaining to software, hardware, databases and networks. Political, social and engineering issues relating to security and privacy. prereq: CS 4821, grad student and instructor consent
CS 8993 - Seminar
Credits: 1.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Presentation and discussion of basic ethical theories, case studies dealing with ethical issues facing the computing professional in his/her life as a practitioner, and the development of research proposal which meets the requirements and standards of the department and serves as the foundation of and guideline for the development of the graduate research project (i.e., thesis). prereq: instructor consent
CHE 5011 - Process Optimization: Lean Six Sigma
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Emphasis on applying Lean and 6 Sigma process design and improvement technicquest, data driven decision making, cultural transformation and effective change communication. prereq: Instructor consent required; credit will not be granted if already received for CHE 5193
CS 5732 - Advanced Computer Security
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Broad, active, hands-on and implementation-based approach to computer security. Fundamental cryptographic theory, advanced techniques and application. Complexity, cryptanalysis, and impact of technological change. Core security theory; confidentiality, integrity, availability. Security models. Risk assessment and decision-making. Issues for general -purpose, trusted and cloud operating system security including hardware requirements, authentication, access control, information flow and assurance. Program and network security fundamentals and best practices including coding principles, firewalls and network design. Exploits, defenses and remediation for multiple issues pertaining to software, hardware, databases and networks. Political, social and engineering issues relating to security and privacy. prereq: CS 4821, grad student and instructor consent
CS 5995 - Special Topics: (Various Titles to be Assigned)
Credits: 1.0 -4.0 [max 8.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Study of selected topic announced in Class Schedule.
EDSE 5000 - Introduction to Post-Secondary Teaching
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Introduction to Teaching will provide a brief overview of learning theory, student and teacher expectations, development of a syllabus, lesson planning goals, rubrics, assignments, student evaluation/assessment, how to submit grades, online teaching using electronic course platforms, classroom management and other topics pertinent to teaching adult learners. This class will provide support for new graduate teaching assistants and new faculty at community colleges. prereq: grad student or community college faculty
EDUC 5413 - Teaching With Technology
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Develops basic computer and educational technology skills focusing on using microcomputers for communications. prereq: 3412 or 5412, min 60 cr or coll grad or instructor consent
EDUC 7002 - Diversity and Social Justice
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Stresses the importance of diversity and exceptionality in educational settings, and its relevance to teaching and learning strategies, assessment, and professional community building. The concepts of privilege and power will be explored from the standpoint of the educator and his/her role in the educational setting. prereq: MEd candidate or instructor consent; credit will not be granted if already received for EHS 7002
EE 5151 - Digital Control System Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Digital control system characteristics: transient and steady-state responses, frequency response, stability. Digital control system design using transform techniques. Controllability and observability. Design of digital control systems using state-space methods: pole placement and observer design, multivariable optimal control. Implementation issues in digital control prereq: 3151; credit will not be granted if already received for 4151
EE 5161 - Linear State-Space Control Systems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
State space representations of control systems and analysis and design. Stability, controllability, observability, realizations, state estimator or observer design and state feedback design. pre-req: 3151 or instructor consent, credit will not be granted if already received for 4161
EE 5311 - Design of VLSI Circuits
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course covers custom design process of very large scale integrated circuits in CMOS technology. pre-req: EE 2212 or instructor consent
EE 5315 - Multiprocessor-Based System Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Parallelism, interconnection networks, shared memory architecture, principles of scalable performance, vector computers, multiprocessors, multicomputers, dataflow architectures, and supercomputers. prereq: 2325; credit will not be granted if already received for 4315
EE 5351 - Introduction to Robotics and Mobile Robot Control Architectures
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Basic concepts and tools for the analysis, design, and control of robotic mechanisms. Topics include basic robot architecture and applications to dynamical systems, mobile mechanisms, kinematics, inverse kinematics, trajectory and motion planning, mobile roots, collision avoidance, and control architectures. prereq: 3151, credit will not be granted if already received for 4351
EE 5477 - Antennas and Transmission Lines
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Theory and performance of antennas and transmission lines. Topics: Allocation of RF spectrum, radiation theory, EM wave propagation, ground effects, interference, antenna performance metrics, transient and sinusoidal transmission line behavior, bounce diagrams, Smith chart, waveguide theory, modeling with the numerical electromagnetics code (NEC), unlicensed wireless applications, specific antenna designs and applications, class demonstrations. prereq: 3445; credit will not be granted if already received for 4477
EE 5479 - Antennas and Transmission Lines Laboratory
Credits: 1.0 [max 1.0]
Prerequisites: 5477 pre or co-req
Grading Basis: A-F or Aud
Typically offered: Every Spring
This laboratory course provides hands-on experience with designing, constructing, and measuring the performance of radio frequency (RF) antennas and transmission lines. Concepts include velocity factor, propagation, factors, characteristic impedance, tuning stubs and matching sections, resonance, parasitic elements, gain, directivity, return loss and RF safety. This course supports the theory presented in EE 5477 (Antennas and Transmission Lines) and is optional for those enrolled in or having completed EE 5477. prereq: 5477 pre or co-req
EE 5501 - Energy Conversion System
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5501/ME 5325
Grading Basis: A-F or Aud
Typically offered: Every Fall
Theory, design and operation of conventional and alternative electrical energy conversion systems. Carbon dioxide cycle, Earth/Sun radiation balance, and environmental impacts. Power delivery systems and integration of conversion systems with the grid. Development of generation portfolios. Impact of energy policies and current energy issues. Case studies. prereq: Chem 1151 or 1153 and 1154
EE 5522 - Power Electronics I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Power semiconductor devices; traditional power converters; ac-dc converters: half-wave and full-wave rectifiers; dc-dc converters: traditional and transformer derived choppers; dc-ac converters: single-phase and three-phase inverters; ac-ac converters; pulse-width modulation; applications. prereq: 3235; credit will not be granted if already received for 4522
EE 5533 - Grid- Resiliency, Efficiency and Technology
Credits: 3.0 [max 3.0]
Prerequisites: 2006 or instructor consent
Grading Basis: A-F or Aud
Typically offered: Every Fall
Concepts and architecture of grid, smart grid and microgrid; resiliency under physical and cyber attacks; grid efficiency via sensors, networks and control; technology including standards and protocols for cybersecurity and protection of the grid; case studies and testbeds. prereq: 2006 or instructor consent
EE 5621 - Microelectronics Technology
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
Various fabrication processes in silicon-based microelectronic circuits and devices: lithography, oxidation, diffusion, thin film deposition, etching and integration of various technologies; material defects analysis and device characterization skills; design of fabrication process with SUPREME IV simulator; fabrication technologies involved in other devices: optical devices, MEMS and semiconductor nanostructures. prereq: 3235, credit will not be granted if already received for 4621 or 5611
EE 5741 - Digital Signal Processing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Discrete linear shift-invariant systems, z- & Fourier transform, sampling, discrete-time processing of signals, reconstruction of analog signals, filters and filter structures in direct, parallel, and cascaded forms, FIR & IIR digital filter design, impulse-invariant, bi-linear transform & window functions, FFT, introduction to image processing. prereq: 2111; credit will not be granted if already received for 4741
EE 5745 - Medical Imaging
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Introduction to the methods and devices for medical imaging, including x-ray imaging, x-ray computer tomography (CT), nuclear medicine (single photon planar imaging, single photon emission computer tomography (SPECT), and positron emission tomography (PET), magnetic resonance imaging (MRI), and ultrasound imaging. The physics and design of systems, typical applications, medical image processing, and tomographic reconstruction. prereq: EE (ECE) 2111, Math 3298 or instructor permission
EE 5765 - Modern Communication
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Design and analysis of modern communication systems; evaluation of analog and digital modulation techniques. (3 hrs lect, 3 hrs lab) prereq: 2111; credit will not be granted if already received for 4765
EE 5801 - Introduction to Artificial Neural Networks
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
General techniques and theory of neural networks, their applications and limitations. The course particularly addresses the design issues and learning algorithms for diverse areas of applications. prereq: CS 1521, Math 3280, Stat 3611 or instructor consent; credit will not be granted if already received for 4801
EE 8151 - Optimal Control Systems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Fall Odd, Spring Even Year
Calculus of variations. Pontryagin minimum principle. Linear quadratic optimal control. Dynamic programming, Hamilton-Jacobi Bellman equation. Constrained optimal control. Linear Quadratic Gaussian control. Kalman filter. prereq: EE 5161; instructor consent
EE 8741 - Digital Image Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Fall Odd Year
Mathematical foundations and practical techniques to process and manipulate images. Students will acquire the ability to analyze two-dimensional images, dealing with mathematical representation of images, image sampling and quantization, Image Transforms, Image Enhancement, Image Restoration, Image Coding, Edge Detection, Texture Analysis, and Compression. prereq: 4741
EE 8765 - Digital Communications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Even Year
Overview of digital data transmission, performance analysis of digital modulation, quadrature multiplexed signaling schemes, signal-space methods in digital data transmission, information theory and block coding, convolutional coding, repeat-request system, spread-spectrum systems, satellite communications. prereq: 5765
ENGL 5802 - English Language for Educators
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Application of linguistic and language learning theories to the teaching of communication arts, with emphasis on preparation of secondary school English teachers. Includes a focus on first and second language acquisition, approaches to language and grammar instruction, and the roles of language and dialect in culture and youth development. prereq: graduate student; credit will not be granted if already received for ENGL 4802
MATH 5201 - Real Variables
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Limits, sequence and series of real numbers, tests for convergence, rearrangements, summability, and the class L-SQUARED. Metric spaces; continuous functions, connectedness, completeness, compactness. Banach fixed-point theorem and Piccard existence theorem for differential equations. prereq: 4201 with a grade of C- or better
MATH 5202 - Applied Functional Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Basic concepts, methods, and applications of functional analysis. Complete metric spaces, contraction mapping, and applications. Banach spaces and linear operators. Inner product and Hilbert spaces, orthonormal bases and expansions, approximation, and applications. Spectral theory of compact operators, including self-adjoint and normal operators. pre-req: MATH 5201, MATH 4326 or 5327; MATH 5327 can be taken concurrently
MATH 5233 - Mathematical Foundations of Bioinformatics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Mathematical, algorithmic, and computational foundations of common tools used in genomics and proteomics. Topics include: sequence alignment algorithms and implementations (Needleman-Wunsch, Smith-Waterman, BLAST, Clustal), scoring matrices (PAM, BLOSUM), statistics of DNA sequences (SNPs, CpG islands, isochores, satellites), and phylogenetic tree methods (UPGMA, parsimony, maximum likelihood). Other topics will be covered as time permits: RNA and protein structure prediction, microarray analysis, post-translational modification prediction, gene regulatory dynamics, and whole-genome sequencing techniques. prereq: MATH 3355, CS 1xxx or above, STAT 3411 or 3611
MATH 5260 - Dynamical Systems
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
Fundamentals of differential equations (existence, uniqueness, continuation of solutions); linear systems, autonomous systems, and Poincare-Bendixson theory; periodic systems; discrete dynamical systems; bifurcation theory; chaos. prereq: 3280 with a grade of C- or better
MATH 5270 - Modeling with Dynamical Systems
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Application and analysis of continuous and discrete dynamical systems. Model construction, simulation, and interpretation. prereq: 3280 with a grade of C- or better
MATH 5280 - Partial Differential Equations
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even Year
Introduction to partial differential equations, emphasizing use of Fourier series, Green's functions, and other classical techniques. prereq: A grade of at least C- in 3280 and 3298 or grad standing
MATH 5327 - Advanced Linear Algebra
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Vector spaces over fields, subspaces, linear transformations, matrix representations, change of basis, inner-product spaces, singular value decomposition, eigenspaces, diagonalizability, annihilating polynomials, Jordan form. prereq: Graduate student or instructor consent
MATH 5330 - Theory of Numbers
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Properties of integers, primes, divisibility, congruences, and quadratic reciprocity. Computational aspects include factoring algorithms and RSA cryptosystem. prereq: 3355 with a grade of C- or better or instructor consent
MATH 5347 - Applied Algebra and Cryptology
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even Year
Applied algebra topics include mathematical origami, permutation games, and the Rubik's cube. Cryptology topics include monoalphabetic substitution ciphers, RSA, primality testing, and elliptic curve cryptology, and recent advancements in the field. Only one of either MATH 4274 or MATH 5374 may be allowed for undergraduate mathematics electives. pre-req: grad student or instructor consent
MATH 5365 - Graph Theory
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Finite graphs, including trees, connectivity, traversability, planarity, colorability, labeling, and matchings. prereq: 3355 with a grade of C- or better or instructor consent
MATH 5366 - Enumerative Combinatorics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Permutations, combinations, binomial coefficients, inclusion-exclusion, recurrence relations, ordinary and exponential generating functions, Catalan numbers, selected topics from designs, finite geometries, Polya's enumeration formula. prereq: 3355 with a grade of C- or better
MATH 5371 - Abstract Algebra I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Introduction to groups and rings and their applications. prereq: 3355 or 4326 with a grade of C- or better or grad standing or instructor consent
MATH 5372 - Abstract Algebra II
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Polynomial rings, divisibility in integral domains, field extensions, finite fields, special topic, and applications. prereq: 5371 with a grade of C- or better or instructor consent
MATH 5810 - Linear Programming
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Motivation problems, modeling, theory of simplex method, duality and sensitivity analysis, large-scale problems, complexity, and Karmarkar algorithm. prereq: 3280 or 4326f with a grade of C- or better
MATH 5830 - Numerical Analysis: Approximation and Quadrature
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Error analysis, interpolation and approximation, numerical integration, solution of nonlinear systems. prereq: 3280 or 4326 with a grade of C- or better, proficiency in FORTRAN or C or C++
MATH 5840 - Numerical Analysis: Systems and Optimization
Credits: 4.0 [max 4.0]
Typically offered: Spring Even Year
Solution of systems of linear equations; elimination and factorization methods; iterative methods; error analysis; eigenvalue/eigenvector approximation; unconstrained optimization; nonlinear least squares. prereq: 3280 or 4326 with a grade of C- or better, proficiency in FORTRAN or C or C++
MATH 5850 - Numerical Differential Equations
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Computational differencing techniques as applied to initial- and boundary-value problems. Introduction to variational formulations of differential equations and general technique of weighed residuals. prereq: 3280 with a grade of C- or better, proficiency in FORTRAN or C or C++
MATH 8201 - Real Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Rigorous development of abstract measure spaces, measurable functions, and corresponding theory of integration. Lebesgue measure and Lebesgue integral developed as a particular model. (offered alt yrs) prereq: 5201 with a grade of C- or better
MIS 5241 - Data Analytics for Managerial Decision Making
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
This course introduces the basic concepts, techniques and technologies of data analytics and business intelligence, and their role in supporting high-level decision making in business. The course examines fundamental principles of descriptive, predictive and prescriptive analytics, illustrates real-world examples in different business contexts using data analytics software, and develops data-analytic thinking in specific application domains. pre-req: MBA student
PHYS 5053 - Data Analysis Methods in Physics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even Year
Problems of data analysis in the context of dynamical models. Emphasis will be placed on large datasets that arise in astrophysics, particle dynamics, physical oceanography and meteorology. (2 hr lect & 2 hr lab) prereq: 2012 or 2015 or 2018 and 2016, 1 sem programming, lab or field experience beyond 2012/2015 and 2016
PSY 5621 - Cognition and Emotion
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Students in this course will read and discuss scholarly reviews and journal articles on theories, research methodology, and topics central to the scientific study of human cognition, emotion, and their applications. There will be discussions on the models of cognitive (perception, memory, language, thinking, and reasoning) and emotional processes and their interrelatedness. Consideration will be given to how these contemporary models are developed and evaluated through empirical studies. Finally, how these theoretical models can be applied to educational, clinical, legal, and workplace settings will be examined. prereq: psychology graduate student or instructor consent
STAT 5411 - Analysis of Variance
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Analysis of variance techniques as applied to scientific experiments and studies. Randomized block designs, factorial designs, nesting. Checking model assumptions. Using statistical computer software. prereq: 3411 or 3611; a grade of C- or better is required in all prerequisite courses
STAT 5511 - Regression Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Simple, polynomial, and multiple regression. Matrix formulation of estimation, testing, and prediction in linear regression model. Analysis of residuals, model selection, transformations, and use of computer software. prereq: 3611, Math 3280 or Math 4326, a grade of C- or better in is required in all prerequisite courses
STAT 5515 - Multivariate Statistics
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
Multivariate normal distribution, MANOVA, canonical correlation, discriminate analysis, principal components. Use of computer software. prereq: 5411 or 5511, Math 3280 or Math 4326, a grade of C- or better in is required in all prerequisite courses
STAT 5521 - Applied Time Series Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Characteristics of time series; time series regression and exploratory data analysis; introduction of ARIMA models, including model building, estimation and forecasting; spectral analysis and filtering. Use of statistical software R. prereq: Math 3280, Stat 3612 or 5511 or instructor consent
STAT 5531 - Probability Models
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Development of probability models and their applications to science and engineering. Classical models such as binomial, Poisson, and exponential distributions. Random variables, joint distributions, expectation, covariance, independence, conditional probability. Markov processes and their applications. Selected topics in stochastic processes. prereq: 3611, Math 1297 or Math 1597, a grade of C- or better in is required in all prerequisite courses, credit will not be granted if already received for STAT 4531.
STAT 5571 - Probability
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Axioms of probability. Discrete and continuous random variables and their probability distributions. Joint and conditional distributions. Mathematical expectation, moments, correlation, and conditional expectation. Normal and related distributions. Limit theorems. prereq: 3611, Math 3298, a grade of C- or better in is required in all prerequisite courses
STAT 5572 - Statistical Inference
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Mathematical statistics; Bayes' and maximum-likelihood estimators, unbiased estimators; confidence intervals; hypothesis testing, including likelihood ratio tests, most powerful tests, and goodness-of-fit tests. prereq: STAT 3612 and 5571 with a grade of C- or better, credit will not be granted if already received for STAT 4572
STAT 8611 - Linear Models
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even Year
Developing statistical theory of general linear model. Distribution theory, testing, and estimation. Analysis of variance and regression. (offered alt yrs) prereq: 5572 with a grade of C- or better
CS 8777 - Thesis Credits: Master's
Credits: 1.0 -24.0 [max 50.0]
Grading Basis: No Grade
Typically offered: Every Fall & Spring
(No description) prereq: Max 18 cr per semester or summer; 10 cr total required (Plan A only)
CS 8794 - Project Credits: Master's
Credits: 1.0 -4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Project credit requirements for the Master's Degree with Project Plan B. Independent research performed under Advisor's supervision. pre-req: graduate student, advisor's consent