System Modelling and Machine Learning

Students can recognize processes as initial-value problems and formulate systems of ordinary differential equations for coupled systems. They can recognize processes as boundary-value problems and formulate systems of ordinary differential equations for coupled systems. They can convert the differential equations into state form and implement and solve them in Python. They can perform frequency analysis of these processes. They can do this for key processes in mechanical, thermal, fluid, and chemical systems.
They know the difference between equation-based and data-based models. They are familiar with supervised learning as opposed to unsupervised learning. They can formulate regression models and classification models with data and build deep neural networks for them. They can use the key tools in sci-kit-learn to build and evaluate such models.