Machine Learning in Materials Lifecycle

Machine Learning in Materials Lifecycle

Level: beginner

Language: English

Format: online

Course duration: 30 hours 

Target groups: Students, professionals, and executives/managers in the fields of general Machine Learning, experimental Materials Science and Engineering, non-ML computational Materials Science.

Availability: Starting soon

This course, “Machine Learning in Materials Lifecycle,” offers a comprehensive introduction to the intersection of machine learning and materials science. Designed for undergraduate students and business professionals with limited technical background, the course spans four 2-hour lectures. It covers the basics of machine learning and materials science, and delves into how machine learning algorithms can be applied across the materials lifecycle—from discovery and development to production, testing, application, and recycling. Through case studies and real-world examples, participants will gain insights into the transformative impact of machine learning on materials science, preparing them for future opportunities and challenges in this interdisciplinary field..

Necessary knowledge

None, but students with a good background in mathematics, physics, and chemistry, as well as basic understanding of materials science and engineering, will find the course easier to follow.

Acquired skills

Students will acquire basic understanding of how machine learning models are applied in Materials Science and Engineering, their possibilities and limitations, skills necessary to get a general understanding of specialized literature on the topic, understanding of applicability of certain machine learning algorithms to specific problems, ability to participate in discussions on the matters of Machine Learning application to materials related problems.

You can meet the lecturer and learn the course structure from this short video

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