Mechanical & Industrial Engrg 590D - Deep Learning/Engineering Appl

This course provides an in-depth exploration of deep learning techniques and their practical applications across various engineering applications. Topics include Feed Forward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Transformers applied to engineering applications such as demand forecasting; route optimization or transportation and delivery; inventory management; dimensionality reduction; anomaly detection in manufacturing and quality control; digital twin modeling; and surrogate of simulation models. Additionally, the course delves into the growing field of Interpretable Machine Learning, ensuring that students learn to not only create powerful models but also focus on techniques for interpretability and explainability. These skills are relevant for the safety critical applications that are typical in engineering, and to make models more transparent and understandable to stakeholders to enhance model deployment. Assignments will include computational problems for hands-on practical experience using established Python libraries and problem analyses, and methodological components that require a deeper understanding of mathematical concepts.

M&I-ENG 422 Open to undergraduate and master's students in MIE. Students should also have knowledge of programming in Python for this course. Instructor permission is required if you do not have this background.