Machine Learning and Artificial Intelligence Methods for Engineers

Catalog description

Engineering practice increasingly relies on computational tools, data analysis, and Machine Learning approaches. This one-semester course introduces machine learning, focusing on supervised learning and its theoretical foundations in the context of engineering practice. Topics include regularized linear models, boosting, kernel methods, deep learning approaches, generative modeling tools, decision-making in stochastic systems, and reinforcement learning approaches.