Teaching

This course covers the mathematical foundations of machine learning, progressing from optimization and linear regression to advanced models, including artificial neural networks, support vector machines, Gaussian processes, and reinforcement learning.

This course is a core mathematics course for graduate students in Mechanical Engineering that covers the foundations of numerical methods. The course combines theory (mathematics) with practice (python coding) by requiring students to implement methods and test them through numerical experiments.