INE599AA Deep Learning for Industrial & Mechanical Engineering

[3–0, 3 cr.]

This course offers a high-level introduction to Deep Learning (DL) tailored for industrial and mechanical engineering students. It focuses on building an intuitive understanding of neural networks and modern deep learning techniques used for images, signals, sequences, and anomaly detection in engineering systems. Through visual explanations, engineering case studies, and guided  demonstrations using pre-built notebooks, students explore core deep learning concepts, including neural network structure, convolutional networks, recurrent networks, autoencoders, and basic generative models, and learn how these tools support tasks such as defect detection, predictive maintenance, quality inspection, and system forecasting. The students will participate in guided  computer-lab sessions to run and interpret DL models using pre-structured tools.

Prerequisites: GNE333 Probability and statistics, COE 212, INE212 or MEE212, 4^th year standing.