Objectives
Subject-specific competence:
After the course, the student can identify research questions in materials science that can be solved by machine learning. They can explain basic data analysis and select suitable representations as input for ML. They can evaluate which ML methods might be best for tackling different materials science problems, and how to improve model performance. They can plan and carry out a ML project on materials data.
Transferable skills:
After the course, the student will gain experience with problem solving, critical thinking and data analytics. They will practice team work, collaboration and scientific communication. They will gain project management skills, practice data visualization and presentation. They will acquire IT skills with computer software for simulations and material visualisation. They will exercise critical thinking, peer and self evaluation and gain experience with giving and receiving feedback.
- Opettaja
Milica Todorovic