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Twin Cities Campus

Robotics M.S.

College of Science and Engineering - Adm
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Minnesota Robotics Institute, Shepherd Laboratories, 100 Union St SE, Minneapolis, MN 55455
Email: mnri@umn.edu
Website: https://cse.umn.edu/mnri
  • Program Type: Master's
  • Requirements for this program are current for Fall 2021
  • Length of program in credits: 31
  • 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.
The Robotics MS program provides a strong foundation in robotics by gathering in a single program the relevant knowledge, expertise, and educational assets such as robot modeling and control, perception using cameras and other sensors, and cognition to reason, plan, and make decisions. Students who graduate from this regular 2-year master’s program will learn the state-of-the-art methods for developing and using robots, be exposed to the cutting-edge technologies and theory forming the basis for the next generation of robots and their applications in areas such as agriculture, underwater exploration, autonomous driving, and manufacturing applications.
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.
Applicants must have a bachelor’s degree from an accredited college or university in an engineering field, computer science, physics, or mathematics.
Other requirements to be completed before admission:
Programming experience including basic algorithms and data structures that are normally taught in beginning computer science courses as part of the undergraduate degree, or subsequent work experience is required. Applicants without some of the background preparation can be admitted, but will be required to complete some of the relevant undergraduate courses in addition to the MS requirements. The GRE is recommended but not required.
Special Application Requirements:
Applications are accepted on a rolling basis.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
  • IELTS
    • Total Score: 6.5
    • Reading Score: 6.5
    • Writing Score: 6.5
Key to test abbreviations (TOEFL, IELTS).
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 21 major credits, up to credits outside the major, and 10 thesis credits. The final exam is oral.
Plan B: Plan B requires 31 major credits and up to credits outside the major. The final exam is written and oral. A capstone project is required.
Capstone Project:The capstone project is completed in consultation with the faculty, or in collaboration with industry partners.
Plan C: Plan C requires 31 major credits and up to credits outside the major. There is no final exam. A capstone project is required.
Capstone Project: Plan C students must complete, in consultation with the advisor, one class project totaling 100 hours or two projects of 50 hours each.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.00 is required for students to remain in good standing.
Courses must be taken on the A-F grade basis, unless only offered S/N.
Required courses (9 credits)
Cognition (3 credits)
Select 3 credits from the following in consultation with the advisor:
CSCI 5511 - Artificial Intelligence I (3.0 cr)
CSCI 5512 - Artificial Intelligence II (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
Perception (3 credits)
Select 3 credits from the following in consultation with the advisor:
CSCI 5561 - Computer Vision (3.0 cr)
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0 cr)
Robot Modeling and Control (3 credits)
Select 3 credits from the following in consultation with the advisor:
AEM 5321 - Modern Feedback Control (3.0 cr)
CSCI 5551 - Introduction to Intelligent Robotic Systems (3.0 cr)
CSCI 5552 - Sensing and Estimation in Robotics (3.0 cr)
EE 5231 - Linear Systems and Control (3.0 cr)
ME 5286 - Robotics (4.0 cr)
Colloquium (1 credit)
Take the following:
ROB 8970 - Robotics Colloquium (1.0 cr)
Electives (10-21 credits)
Plan A students select 10 to 11 credits, Plan B students select 14 to 18 credits, and Plan C students select 20 to 21 credits from the following in consultation with the advisor. Up to 3 credits of ROB 5994 can be applied to degree requirements. If CSCI 5980 or 8980 Special Topics is selected, only topics in the areas of robotics, computer vision, and cognition may be used. Other courses may be selected with approval of the advisor and director of graduate studies.
AEM 5321 - Modern Feedback Control (3.0 cr)
AEM 5333 - Design-to-Flight: Small Uninhabited Aerial Vehicles (3.0 cr)
AEM 5451 - Optimal Estimation (3.0 cr)
AEM 8411 - Advanced Dynamics (3.0 cr)
AEM 8421 - Robust Multivariable Control Design (3.0 cr)
AEM 8423 - Convex Optimization Methods in Control (3.0 cr)
AEM 8495 - Advanced Topics in Aerospace Systems (1.0-4.0 cr)
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
CSCI 5511 - Artificial Intelligence I (3.0 cr)
CSCI 5512 - Artificial Intelligence II (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
CSCI 5551 - Introduction to Intelligent Robotic Systems (3.0 cr)
CSCI 5552 - Sensing and Estimation in Robotics (3.0 cr)
CSCI 5561 - Computer Vision (3.0 cr)
CSCI 5563 - Multiview 3D Geometry in Computer Vision (3.0 cr)
CSCI 5609 - Visualization (3.0 cr)
CSCI 5619 - Virtual Reality and 3D Interaction (3.0 cr)
CSCI 5980 - Special Topics in Computer Science (1.0-3.0 cr)
CSCI 8581 - Big Data in Astrophysics (4.0 cr)
CSCI 8980 - Special Advanced Topics in Computer Science (1.0-3.0 cr)
EE 5231 - Linear Systems and Control (3.0 cr)
EE 5235 - Robust Control System Design (3.0 cr)
EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
EE 5251 - Optimal Filtering and Estimation (3.0 cr)
EE 5391 {Inactive} (3.0 cr)
EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0 cr)
EE 5621 - Physical Optics (3.0 cr)
EE 5622 - Physical Optics Laboratory (1.0 cr)
EE 5624 - Optical Electronics (4.0 cr)
EE 5705 - Electric Drives in Sustainable Energy Systems (3.0 cr)
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory (1.0 cr)
EE 5940 - Special Topics in Electrical Engineering I (1.0-4.0 cr)
EE 8215 - Nonlinear Systems (3.0 cr)
EE 8231 - Optimization Theory (3.0 cr)
EE 5571 - Statistical Learning and Inference (3.0 cr)
EE 8591 - Predictive Learning from Data (3.0 cr)
ME 5241 - Computer-Aided Engineering (4.0 cr)
ME 5243 - Advanced Mechanism Design (4.0 cr)
ME 5248 - Vibration Engineering (4.0 cr)
ME 5286 - Robotics (4.0 cr)
ME 8281 - Advanced Control System Design-1 (3.0 cr)
ME 8283 - Design of Mechatronic Products (4.0 cr)
ME 8285 - Control Systems for Intelligent Vehicle Applications (3.0 cr)
ROB 5994 - Directed Research (1.0-3.0 cr)
Plan Options
Plan A (10 credits)
Take 10 thesis credits.
ROB 8777 - Thesis Credits Master's (1.0-18.0 cr)
-OR-
Plan B (3 to 6 credits)
Take at least 3 credits of the following in consultation with advisor:
ROB 8760 - Capstone Project (1.0-3.0 cr)
 
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CSCI 5511 - Artificial Intelligence I
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4511W/CSci 5511
Prerequisites: [2041 or #], grad student
Typically offered: Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 5512W/CSci 5512
Typically offered: Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods. prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
CSCI 5561 - Computer Vision
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: CSci 5511, 5521, or instructor consent.
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5561/EE 8541
Typically offered: Every Spring
Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks. prereq: [4541, 5581, CSE grad student] or instr consent
AEM 5321 - Modern Feedback Control
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5321/EE 5231
Typically offered: Every Fall
State space theory for multiple-input-multiple-output aerospace systems. Singular value decomposition technique, applications to performance/robustness. Linear quadratic gaussian and eigenstructure assignment design methods. Topics in H[infinity symbol]. Applications. prereq: 4321 or EE 4231 or ME 5281 or equiv
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics. prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping. prereq: [5551, Stat 3021] or instr consent
EE 5231 - Linear Systems and Control
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The course studies finite-dimensional linear systems in continuous and discrete time. Such systems are described by ordinary differential and difference equations. Input-output and state-space descriptions are provided and analyzed. Introductory methods for controlling such systems are developed. prereq: [3015, CSE grad student] or instr consent
ME 5286 - Robotics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
The course deals with two major components: robot manipulators (more commonly known as the robot arm) and image processing. Lecture topics covered under robot manipulators include their forward and inverse kinematics, the mathematics of homogeneous transformations and coordinate frames, the Jacobian and velocity control, task programming, computational issues related to robot control, determining path trajectories, reaction forces, manipulator dynamics and control. Topics under computer vision include: image sensors, digitization, preprocessing, thresholding, edge detection, segmentation, feature extraction, and classification techniques. A weekly 2 hr. laboratory lasting for 8-9 weeks, will provide students with practical experience using and programming robots; students will work in pairs and perform a series of experiments using a collaborative robot. prereq: [3281 or equiv], [upper div ME or AEM or CSci or grad student]
ROB 8970 - Robotics Colloquium
Credits: 1.0 [max 2.0]
Grading Basis: S-N only
Typically offered: Every Fall
Recent developments in robotics and related disciplines.
AEM 5321 - Modern Feedback Control
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5321/EE 5231
Typically offered: Every Fall
State space theory for multiple-input-multiple-output aerospace systems. Singular value decomposition technique, applications to performance/robustness. Linear quadratic gaussian and eigenstructure assignment design methods. Topics in H[infinity symbol]. Applications. prereq: 4321 or EE 4231 or ME 5281 or equiv
AEM 5333 - Design-to-Flight: Small Uninhabited Aerial Vehicles
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Spring
Designing, assembling, modeling, simulating, testing/flying of uninhabited aerial vehicles. Rapid prototyping software tools for vehicle modeling. Guidance, navigation, flight control, real-time implementations, hardware-in-the-loop simulations, flight tests. prereq: [[4202, concurrent registration is required (or allowed) in 4303W, 4601] or equiv], instr consent
AEM 5451 - Optimal Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Fall Even Year
Basic probability theory. Batch/recursive least squares estimation. Filtering of linear/non-linear systems using Kalman and extended Kalman filters. Applications to sensor fusion, fault detection, and system identification. prereq: [[MATH 2243 or STAT 3021 or equiv], [4321 or EE 4231 or ME 5281 or equiv]] or instr consent
AEM 8411 - Advanced Dynamics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Advanced analytical mechanics and non-linear dynamical systems. Review of Lagrangian mechanics. Hamilton's equations of motion. Canonical transformations and Hamilton-Jacobi theory. Kane's equations. Analysis of differential equations and numerical methods. Phase plane, averaging, and perturbation methods. Stability/bifurcations of equilibria. prereq: 5401 or equiv
AEM 8421 - Robust Multivariable Control Design
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Application of robust control theory to aerospace systems. Role of model uncertainty/modeling errors in design process. Control analysis and synthesis, including H[sub2] and H[infinity symbol] optimal control design and structural singular value [Greek letter mu] techniques. prereq: 5321 or equiv
AEM 8423 - Convex Optimization Methods in Control
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Practical aspects of convex optimization methods applied to solve design/analysis problems in control theory. prereq: 5321 or EE 5231 or equiv
AEM 8495 - Advanced Topics in Aerospace Systems
Credits: 1.0 -4.0 [max 32.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall, Spring & Summer
Individual student projects completed under guidance of a faculty sponsor. prereq: dept consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads. prereq: 4041 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4511W/CSci 5511
Prerequisites: [2041 or #], grad student
Typically offered: Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 5512W/CSci 5512
Typically offered: Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods. prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics. prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping. prereq: [5551, Stat 3021] or instr consent
CSCI 5561 - Computer Vision
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: CSci 5511, 5521, or instructor consent.
CSCI 5563 - Multiview 3D Geometry in Computer Vision
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
The 3D spatial relationship between cameras and scenes in computer vision. Application to tasks such as planning robots, reconstructing scenes from photos, and understanding human behaviors from body-worn cameras data. Multiview theory fundamentals, structure-from-motion, state-of-the-art approaches, and current research integration. Prereq: Students enrolling in this course must have completed CSCI 5561 or have instructor consent.
CSCI 5609 - Visualization
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent
CSCI 5619 - Virtual Reality and 3D Interaction
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Introduction to software, technology/applications in virtual/augmented reality, 3D user interaction. Overview of current research. Hands-on projects. prereq: 4611 or 5607 or 5115 or equiv or instr consent
CSCI 5980 - Special Topics in Computer Science
Credits: 1.0 -3.0 [max 27.0]
Typically offered: Periodic Fall & Spring
Lectures and informal discussions on current topics in computer science. prereq: instr consent; may be repeated for cr
CSCI 8581 - Big Data in Astrophysics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast/Stat/CSci 8581/Phys 8581
Grading Basis: A-F only
Typically offered: Every Spring
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent. Suggested: familiarity with astrophysics topics such as star formation and evolution, galaxies and clusters, composition and expansion of the universe, gravitational wave sources and waveforms, and high-energy astrophysics.
CSCI 8980 - Special Advanced Topics in Computer Science
Credits: 1.0 -3.0 [max 27.0]
Typically offered: Every Fall & Spring
Lectures and informal discussions. prereq: instr consent
EE 5231 - Linear Systems and Control
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The course studies finite-dimensional linear systems in continuous and discrete time. Such systems are described by ordinary differential and difference equations. Input-output and state-space descriptions are provided and analyzed. Introductory methods for controlling such systems are developed. prereq: [3015, CSE grad student] or instr consent
EE 5235 - Robust Control System Design
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Development of control system design ideas; frequency response techniques in design of single-input/single-output (and MI/MO) systems. Robust control concepts. CAD tools. prereq: CSE grad, 3015, 5231 or instr consent
EE 5239 - Introduction to Nonlinear Optimization
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent
EE 5251 - Optimal Filtering and Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Every Fall
Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications. prereq: [[[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; 3025, 4231 recommended
EE 5542 - Adaptive Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Design, application, and implementation of optimum/adaptive discrete-time FIR/IIR filters. Wiener, Kalman, and Least-Squares. Linear prediction. Lattice structure. LMS, RLS, and Levinson-Durbin algorithms. Channel equalization, system identification, biomedical/sensor array processing, spectrum estimation. Noise cancellation applications. prereq: [4541, 5531, CSE grad student] or dept consent
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5561/EE 8541
Typically offered: Every Spring
Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks. prereq: [4541, 5581, CSE grad student] or instr consent
EE 5621 - Physical Optics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Physical optics principles, including Fourier analysis of optical systems/images, scalar diffraction theory, interferometry, and coherence theory. Diffractive optical elements, holography, astronomical imaging, optical information processing, microoptics. prereq: [3015, CSE grad student] or dept consent
EE 5622 - Physical Optics Laboratory
Credits: 1.0 [max 1.0]
Typically offered: Every Spring
Fundamental optical techniques. Diffraction and optical pattern recognition. Spatial/temporal coherence. Interferometry. Speckle. Coherent/incoherent imaging. Coherent image processing. Fiber Optics. prereq: [[5621 or concurrent registration is required (or allowed) in 5621], CSE grad student] or dept consent
EE 5624 - Optical Electronics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Fundamentals of lasers, including propagation of Gaussian beams, optical resonators, and theory of laser oscillation. Polarization optics, electro-optic, acousto-optic modulation, nonlinear optics, phase conjugation. prereq: [[3601 or Phys 3002], CSE grad student] or dept consent
EE 5705 - Electric Drives in Sustainable Energy Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Role of electric drives in wind-electric systems, inertial storage, elec/hybrid vehicles. AC machines for energy-efficient operation using d-q axis modeling. Vector-/direct-torque-controlled induction motor drives. Permanent-magnet and interior-permanent magnet ac motor drives. Sensorless drives. Voltage space-vector modulation technology. prereq: [4701, CSE grad student] or dept consent
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory
Credits: 1.0 [max 1.0]
Typically offered: Periodic Spring
Lab to accompany 5705. prereq: 5705 or concurrent registration is required (or allowed) in 5705
EE 5940 - Special Topics in Electrical Engineering I
Credits: 1.0 -4.0 [max 12.0]
Typically offered: Every Fall, Spring & Summer
Special topics in electrical and computer engineering. Topics vary.
EE 8215 - Nonlinear Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Current topics in stability analysis of nonlinear systems, design of controllers for nonlinear systems, discrete-time and stochastic nonlinear systems. prereq: instr consent
EE 8231 - Optimization Theory
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Introduction to optimization in engineering; approximation theory. Least squares estimation, optimal control theory, and computational approaches. prereq: instr consent
EE 5571 - Statistical Learning and Inference
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Deterministic and random approaches to learning and inference from data, with applications to statistical models for estimation, detection, and classification. Algorithms and their performance include minimum-variance unbiased estimators, sufficient statistics, fundamental bounds, (non)linear least-squares, maximum-likelihood, expectation-maximization, nonparametric density estimators, mean-square error and Bayesian estimators, importance sampling, Kalman and particle filtering, sequential probability ratio test, bootstrap, Monte Carlo Markov Chains, and graphical models. prereq: courses in Stochastic Processes (EE 5531) and Digital Signal Processing (EE 4541)
EE 8591 - Predictive Learning from Data
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of ‘new’ algorithms. prereq: CSE grad student or instr consent
ME 5241 - Computer-Aided Engineering
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Apply computer-aided engineering to mechanical design. Engineering design projects and case studies using computer-aided design and finite element analysis software; design optimization and computer graphical presentation of results. prereq: 3222, CSci 1113 or equiv, CSE upper div or grad
ME 5243 - Advanced Mechanism Design
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Summer
Analytical methods of kinematic, dynamic, and kinetoelastodynamic analysis and synthesis of mechanisms. Computerized design for function, path, and motion generation based on Burmeister theory. prereq: CSE upper div or grad, 3222 or equiv, basic kinematics and dynamics of machines; knowledge of CAD packages such as Pro-E recommended
ME 5248 - Vibration Engineering
Credits: 4.0 [max 4.0]
Typically offered: Periodic Summer
Apply vibration theory to design; optimize isolators, detuning mechanisms, viscoelastic suspensions and structures. Use modal analysis methods to describe free vibration of complex systems, relating to both theoretical and test procedures. prereq: CSE upper div or grad, 3281
ME 5286 - Robotics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
The course deals with two major components: robot manipulators (more commonly known as the robot arm) and image processing. Lecture topics covered under robot manipulators include their forward and inverse kinematics, the mathematics of homogeneous transformations and coordinate frames, the Jacobian and velocity control, task programming, computational issues related to robot control, determining path trajectories, reaction forces, manipulator dynamics and control. Topics under computer vision include: image sensors, digitization, preprocessing, thresholding, edge detection, segmentation, feature extraction, and classification techniques. A weekly 2 hr. laboratory lasting for 8-9 weeks, will provide students with practical experience using and programming robots; students will work in pairs and perform a series of experiments using a collaborative robot. prereq: [3281 or equiv], [upper div ME or AEM or CSci or grad student]
ME 8281 - Advanced Control System Design-1
Credits: 3.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Loop Shaping. Review of controllability/observability. LQR/LQG/LTR. Repetitive control. Input shaping. Tracking control (feedforward, precompensation). Lyapunov stability. System identification. prereq: 5281
ME 8283 - Design of Mechatronic Products
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Fall Odd Year
The purpose of this course is for advanced mechanical engineering students to gain additional mechatronic skills by learning how to use microcontrollers to implement control systems in the context of a practical product or device. Embedded microcontrollers are ubiquitous in modern products from washing machines to cell phones to automobiles to space rockets. Knowing how to design and program microcontrollers, how to interface microcontrollers to sensors and actuators, and how to implement control algorithms on a microcontroller is an important skill for the modern control system design engineer. The course is hands-on and follows a learn by doing approach. Students spend 1/3 the course in a microcontroller boot camp and 2/3 on a substantial microcontroller project. The lectures cover didactic material related to microcontrollers, sensors, actuators, electronics circuit design and fabrication and control algorithm implementation. prereq: An introductory system dynamics and controls course or permission of instructor.
ME 8285 - Control Systems for Intelligent Vehicle Applications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course focuses on a study of several advanced control design techniques and their applications to smart vehicles. The control system topics studied include lead and lag compensator design, loop shaping, analysis of system norms, H2-optimal control, feedback linearization, sliding surface control, and observer design. The vehicle application topics studied include cruise control, adaptive cruise control, automated lane keeping, automated highway systems, yaw stability control, active rollover prevention, engine control, and active and semi-active suspensions. In each application, a dynamic model is first developed that is simple enough for control system design, but at the same time, rich enough for capturing the essential features of the dynamics. The control design for each application is studied in-depth during lecture and further analyzed during hands-on homework. prereq: 5281 or EE 5231 or equiv
ROB 5994 - Directed Research
Credits: 1.0 -3.0 [max 9.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Directed research arranged with faculty member.
ROB 8777 - Thesis Credits Master's
Credits: 1.0 -18.0 [max 50.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Master's thesis credits.
ROB 8760 - Capstone Project
Credits: 1.0 -3.0 [max 6.0]
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
Project arranged between student and faculty.