Learning Objectives
After completion of the course you will be able to:
- Identify research questions in material science (MS) that can be solved by machine learning (ML)
- Understand different types of MS datasets for ML
- Perform basic data analysis of datasets
- Select a suitable MS data representation as input for ML
- Consider which ML methods might be best for tackling different MS problems
- Assess and improve the performance of the ML model
- Carry out a computational project on ML for MS
- Critically comment on ML applications in MS (on quality of data analysis, suitability of chosen ML method, quality of assessment of ML performance, etc)
After completion of the course you will be able to:
- Identify research questions in material science (MS) that can be solved by machine learning (ML)
- Understand different types of MS datasets for ML
- Perform basic data analysis of datasets
- Select a suitable MS data representation as input for ML
- Consider which ML methods might be best for tackling different MS problems
- Assess and improve the performance of the ML model
- Carry out a computational project on ML for MS
- Critically comment on ML applications in MS (on quality of data analysis, suitability of chosen ML method, quality of assessment of ML performance, etc)
- Peppi-Teacher: Milica Todorovic