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..

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.

Dates: 5-13.12.2023
Lecturer: Oleksandr Vasiliev, Head of the Department of Applied Mathematics and Computational Experiment in Materials Science, Franzevich Institute for Problems of Materials Science, National Academy of Sciences of Ukraine.

Teaching format: self-study based on video and text materials on the platform eduportal.kau.org.ua, passing the final test.

Registration: please fill in the form to register for the course. After filling it out, you will receive instructions for registration on the portal eduportal.kau.org.ua and will be enrolled in the course of your choice.

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

The course is free of charge. Upon completion of the course, all participants will receive certificates indicating the number of ECTS credits.