3.0

credits

Average Course Rating

This course introduces core concepts and modern techniques in artificial intelligence and data science, with a focus on applications in materials science. Topics include classification, regression, clustering, and generative AI, using models such as random forests, neural networks (NN), convolutional neural networks (CNN), graph neural networks (GNN), and transformer-based architectures (e.g., GPT). Students will work with real-world materials datasets derived from multiscale modeling and experimental measurements, including tabular, image-based, and structural formats. Hands-on coding exercises and a final project will reinforce practical applications.