This course covers the theoretical underpinnings of the various intelligent techniques used in manufacturing, practical know-how needed to quickly and powerfully program and apply these techniques to data, and some of the best practices of intelligent techniques in broad areas including robotics, text recognition, computer vision, image processing, and medical informatics. The course is open to undergraduate and graduate students.

This course provides an in-depth introduction to ocean renewable energy systems. It covers the primary options for converting the available energy in the offshore environment into electricity as well as other technologies, infrastructure and processes that are necessary to make such conversion practical. The conversion technologies that will be considered include offshore wind energy, wave energy, ocean currents, tidal energy, ocean thermal energy conversion (OTEC), and floating solar photovoltaics.

The course introduces the theoretical background to hydrodynamics, gravity waves in the oceans, coastal processes and tides.

This course offers an examination of the principles of systems engineering (SE) and their application within engineering management contexts. Students will be introduced to the vocabulary and the core concepts and techniques (tools) of SE. Of particular focus is exploring how systems thinking, as a perspective, offers a valuable capability for holistically understanding and dealing with engineering management problems and challenges.

This course provides an overview of the fundamental concepts of basic accounting and finance, focusing on their application to managing engineering projects and organizations. Key topics covered include basic accounting terminology and methods, financial statements and how to interpret them, sources of finance available to businesses, engineering project accounting and financing, and personal and corporate financing basics.

Prescriptive analytics is the process of utilizing and analyzing data to make "optimal" decisions. In this course we will build optimization tools for data-driven decision making using Python along with the Gurobi optimizer (available freely for teaching purposes). This course will start with an introduction to both Python and optimization modelling. We will rapidly progress to building large linear programming and mixed integer programming models that are often used for decision making in data-intensive businesses.

Data analytics, statistical/machine learning, and predictive modeling are now used widely in all fields. The purpose of this course is to provide introductory knowledge that will help students understand the fundamentals of the field and use software packages to solve problems. The emphasis will be on applying the statistical methods to real data sets. The 600-level class will require students to explore topics in more depth.

This course covers the fundamental principles behind characterization approaches such as electron microscopy, x-ray diffraction, atomic force microscopy, and synchrotron techniques. This is typically supplemented by some in lab demonstrations of the techniques, where a dataset is generated, and students write a report (3 pages) forming conclusions based on the foundation they learned in lecture. By the end of the semester students should be able to make an informed selection of the appropriate characterization methods to address a specific research challenge.

Not available at this time

In this course, we will study the major concepts, challenges, and solution strategies to engineer the logistics of the supply chain. We will focus on the modeling and rigorous analysis of problems related to the efficient design and operation of the supply chain. Topics include facility location, routing and transportation, inventory management, and supply chain strategies. We will use various techniques to solve these problems, such as mixed-integer programming, dynamic programming, non-linear optimization, and game theory. Both deterministic and stochastic scenarios will be considered.