CHEME 5820

Machine Learning and Artificial Intelligence Methods for Engineers

Course Description

Course information provided by the Courses of Study 2024-2025.

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.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: CHEME 4800 or CHEME 5800. 

• Demonstrate mastery of basic machine learning principles and applications in the context of engineering practice.
• Demonstrate the ability to implement basic machine learning models, apply them to real-world data sets, and evaluate their performance.
• Demonstrate an understanding of the impact of assumptions on the applicability of machine learning methods, identify which settings various methods apply, and analyze the strengths and weaknesses of methods in different applications.

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