In this course, we will explore the fundamentals of Machine Learning as applied to Chemical Engineering.

Machine learning (ML) has emerged as a transformative tool in various fields, and its application in chemical engineering is no exception. This course is designed to provide students with a comprehensive understanding of how ML techniques can be applied to solve complex problems in the chemical engineering domain. 

As industries seek to optimize processes, reduce costs, and enhance safety, the ability to analyze vast amounts of data and extract meaningful insights has become increasingly crucial. Machine learning offers powerful methods for modeling, predicting, and optimizing chemical processes, allowing engineers to innovate and improve efficiency in ways that were previously unimaginable.

In this course, students will learn the fundamental principles of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. We will explore how these techniques can be applied to typical challenges in chemical engineering, such as process modeling, quality control, and materials discovery. Through theoretical lectures and hands-on projects, students will gain practical experience applying ML algorithms to real-world chemical engineering problems.

By the end of this course, students will be equipped with the skills to leverage machine learning in their future careers, contributing to the advancement of chemical engineering practices in academia, industry, and beyond.