CHE456: Applied Machine Learning in Chemical Engineering

This course provides a comprehensive introduction to machine and deep learning techniques tailored to chemical engineering applications. Students will learn the theoretical foundations of machine and deep learning algorithms (e.g., classification, regression, clustering) and acquire practical skills in implementing data-driven models for process monitoring, reactor optimization, materials design, and energy systems. Emphasis is placed on hands-on learning using modern software tools (e.g., Python, TensorFlow, PyTorch), alongside advanced topics such as reinforcement learning and agentic AI. By the end of the course, students will be able to select and apply appropriate machine and deep learning solutions to real-world chemical engineering challenges, interpret model performance, and communicate their findings in a scientific and ethical manner.