This course offers a theoretical and hands-on introduction to Quantum Computing for modeling and implementing solutions for engineering and sciences applications.

This course offers a hands-on, code-first introduction to Deep Learning for predictive engineering applications encompassing the latest advances in deep neural networks and big data analytics in the field of reliability, maintenance, and risk.

This course provides a detailed account of data analytics methods for reliability and risk data from three different yet complementary perspectives: frequentist approaches, Bayesian inference, and machine learning methods.

The course concentrates on the analytical tools and methodologies to quantify various risks and how businesses can make decisions in light of risk and uncertainty. Topics include statistical methods, optimizations and simulations.

This course revolves around exploiting (1) a detailed understanding of the physics of failure (PoF) to accelerate the reliability analysis of systems through the design of reliability tests, and (2) leveraging testing and design modification to facilitate ‘reliability growth.’

This course provides a semi-intensive survey of the field of structural reliability engineering.

This course is an introduction to human reliability analysis, incorporating human factors that govern performance.

This course provides an introduction to simulation methodologies and model development approaches that provide insight into the systemic risks or performance characteristics involved in random processes and complex systems.

This course teaches the fundamental mathematical and statistical concepts that are necessary for many engineering applications including the characterization of uncertainty for risk and reliability applications.