Lecture materials of the On-line course on Big Data and Artificial Intelligence in Materials Sciences
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Lecture 1 |
Matthias Scheffler | General Introduction to big-data-driven materials science | Part 1 PDF | Video Part 2 PDF | Video |
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Lecture 2 |
Claudia Draxl | NOMAD Repository, Archive, Encyclopedia | PDF | Video |
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Lecture 3 |
Luca Ghiringhelli | Introduction to artificial intelligence and machine-learning methods | |
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Lecture 4 Nov. 26, 2020 |
Luca Ghiringhelli | Compressed sensing meets symbolic regression: SISSO | |
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Lecture 5 Dec. 3, 2020 |
Daniel Speckhard | Decision trees and random forests |
Whiteboard |
| Lecture 6 Dec. 10, 2020 |
Santiago Rigamonti | Regularized Regression and kernel methods | |
| Lecture 7 Dec. 17, 2020 |
Luigi Sbailò | Unsupervised learning | |
| Lecture 8 Jan. 7, 2021 |
Angelo Ziletti | Artificial Neural Networks and Deep Learning - Part 1 |
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| Lecture 9 Jan. 14, 2021 |
Angelo Ziletti | Artificial Neural Networks and Deep Learning - Part 2 | |
| Lecture 10 Jan. 21, 2021 |
Claudia Draxl | Materials data, 4V, FAIR principles | |
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Lecture 11 |
Matthias Scheffler | Subgroup discovery, rare-phenomena challenge, and domain of applicability |
Part 1 PDF | Video |
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Lecture 12 |
Jilles Vreeken | Interpretability and Causality |
PDF | Video
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Lecture 13 |
Rampi Ramprasad | Applications in real materials | |
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Christoph T. Koch | AI in experiment | |
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Lecture 15 |
Lucas Foppa | Fusion of experimental and computational data by AI |