| 1. |
Overview of Materials Informatics |
Learn about Materials Informatics |
100minutes |
| 2. |
What Is Materials Data? |
Think about what kinds of materials data may exist |
100minutes |
| 3. |
Hands-on Practice with Colab and Python |
Open Google Colab using your Google account |
100minutes |
| 4. |
Basic Statistics for Materials Data Analysis |
Review the concepts of mean, maximum, minimum, and variance |
100minutes |
| 5. |
Prediction of Material Properties Using Linear Regression |
Learn what it means to “approximate data with a straight line” |
100minutes |
| 6. |
Classification Problems |
Think of examples that can be judged as “yes / no” |
100minutes |
| 7. |
Model Evaluation and Overfitting |
Explain what overfitting is |
100minutes |
| 8. |
Fundamentals of Feature Engineering |
Think about useful information other than composition (e.g., atomic radius, melting point, crystal structure)
|
100minutes |
| 9. |
Practical Model Evaluation |
Think about what “accuracy” means in modeling |
100minutes |
| 10. |
Successful Applications of Materials Informatics |
Learn about Lasso analysis |
100minutes |
| 11. |
Group Project Introduction and Topic Selection |
Identify one materials-related field where AI is likely being used |
100minutes |
| 12. |
Group Work ①: Data Preparation and Analysis |
Review the group project |
100minutes |
| 13. |
Group Work ②: Interpretation and Presentation Preparation |
Review the group project |
100minutes |
| 14. |
Group Presentations |
Consider what can and cannot be done using Materials Informatics |
200minutes |
| Total. |
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1500minutes |