Johns Hopkins University 3 credits Materials Science & Engineering
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.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.