Graduate Coursework

The University of Michigan Robotics Program is a multi-disciplinary program, incorporating coursework across the College of Engineering.  Below are course descriptions, copied from the corresponding school's course guide.

GPA: 3.825/4.000

Fall 2015

IOE536 – Cognitive Ergonomics
Theories and concepts of human information processing are introduced to analyze human perceptual and cognitive performance in human machine information systems such as intelligent transportation and manufacturing systems. Conceptual and quantitative models, interface design techniques and research and evaluation methods are presented. Samples of on-going research are also discussed.

ROB501 - Mathematics for Robotics
Applied mathematics for robotics engineers. Topics include vector spaces, orthogonal bases, projection theorem, least squares, matrix factorizations, Kalman filter and extensions, particle filters, underlying probabilistic concepts, norms, convergent sequences, contraction mappings, Newton Raphson algorithm, nonlinear constrained optimization, local vs global convergence, convexity, linear and quadratic programs, and randomized search strategies.

ROB550 – Robotic Systems Laboratory
Multidisciplinary laboratory course with exposures to sensing, reasoning, and acting for physically-embodied systems. Intro to kinematics, localization and mapping, planning, control, user interfaces. Design, build, integration, and test of mechanical, electrical, and software systems. Projects based on a series of robotic platforms: manipulators, mobile robots, aerial or underwater vehicles.


Winter 2016

AERO552 - Aerospace Information Systems
Information systems for Aerospace applications. Data abstraction, elementary data structures. Graphs, automata theory. Life cycle models, validation & verification. Deterministic search algorithms. Decision making under uncertainty; review of probability theory, introduction to information theory, Bayesian Networks, Markov chains, Markov Decision Processes. Substantial code development in a traditional programming language.

EECS461 – Embedded Control Systems
Basic interdisciplinary concepts needed to implement a microprocessor based control system. Sensors and actuators. Quadrature decoding. Pulse width modulation. DC motors. Force feedback algorithms for human computer interaction. Real time operating systems. Networking. Use of MATLAB to model hybrid dynamical systems. Autocode generation for rapid prototyping. Lecture and laboratory.

ROB590 – Independent Study
Independent study research with Professor Nadine Sarter focuses on the process of trust calibration in human-robot systems. 

Fall 2016

EECS592 - Foundations of Artificial Intelligence
An advance introduction to AI emphasizing its theoretical underpinnings. Topics include search, logic, knowledge representation, reasoning planning, decision making under uncertainty, and machine learning.

PSYCH613 - Advanced Statistical Methods I
This is a two-term course (with PSYCH 614 in the Winter term). PSYCH 613 is a prerequisite for PSYCH 614. Students will gain experience by analyzing data and gain an appreciation for the rationale underlying the standard statistical procedures used in psychological research. The course consists of four hours of lecture; additional review sections will also be available. Topics covered throughout the year include analysis of variance, regression, factor analysis, multidimensional scaling, and clustering.

SI588 - Fundamentals of Human Behavior
Surveys basic principles of cognitive and social psychology relevant to the design and use of information systems. Focuses on important findings in psychological science and their implications for the design and use of information systems. Topics include the basics of human perception, memory capacity and organization, the development of skill and expertise, and the characteristics of everyday reasoning and decision making. For example, a central problem in information science is how to label information stored for later recall. By examining how human memory operates, we can gain some insight into possible schemes that may be compatible with human users. This survey of what we know about the human mind offers ideas about how to exploit mental capacities in the design and use of information systems.

Winter 2017

IOE434 - Human Error and Complex System Failures
Introduction to a new systems-oriented approach to safety management and the analysis of complex system failures. The course covers a wide range of factors contributing to system failures: human perceptual and cognitive abilities and limitations, the design of modern technologies and interfaces, and biases in accident investigation and error analysis. Recent concepts in the area of high reliability organizations and resilience engineering are reviewed. Students perform systems analysis of actual mishaps and disasters in various domains, including various modes of transportation, process control and health care.

PSYCH614 - Advanced Statistical Methods II
This is a two-term course (with PSYCH 614 in the Winter term). PSYCH 613 is a prerequisite for PSYCH 614. Students will gain experience by analyzing data and gain an appreciation for the rationale underlying the standard statistical procedures used in psychological research. The course consists of five hours of lecture; additional review sections will also be available. Topics covered throughout the year include analysis of variance, regression, factor analysis, multidimensional scaling, clustering, and structural equations modeling. Students will also analyze data from their own research projects as well as design studies in their own area of research.

ROB590 - Independent Study
Independent study research with Professor Nadine Sarter focuses on how transparency may be used to support appropriate trust calibration in human-robot systems.