Recommended courses

Convex Optimization for Engineers (Hidalgo-Gonzalez)

This course focuses on convex optimization theory, convexification of non-convex problems, engineering applications, modeling and implementation in a programming language (MATLAB or choice). This course covers: convex sets and functions, convex optimization problems (LP, QP, SOCP, SDP, robust and stochastic optimization), weak and strong duality, optimality conditions (complementary slackness, Karush-Kuhn-Tucker), and solution and shadow price interpretation. Some applications include: design in mechanical engineering, optimal control problems, machine learning, energy, transportation, etc.

Prerequisites: nongraduate students may enroll with consent of instructor.

Modern Power Systems (Hidalgo-Gonzalez)

The objective of this seminar is to introduce students to the research field of integration of renewable energy (RE) in power systems. This course covers some relevant literature and the state of the art in the following subareas related to RE integration: capacity expansion models, optimal power flow, power dynamics, electricity markets, RE variability and forecasting, learning and AI for power systems, microgrids and islanded grids, electrical vehicles and demand response, among others.

Prerequisites:  nongraduate students may enroll with consent of instructor.

Fundamentals of Energy Systems (Tynan, Victor)

Electric Power Systems Modeling (Davidson)

Data Science for Power Systems (Shi)

Introduction to Solar and Wind Energy

Advanced control theory courses:

Linear systems theory, Nonlinear systems, Optimal control, Robust control, Optimal estimation, Multiagent systems, Complexity and large-scale systems, etc.

ML/AI courses:

Introduction to machine learning, Deep learning, Data mining and predictive analytics, Machine learning for physical systems, etc.

For more information, visit MAE courses’ site.