├── README.md ├── apt.txt ├── bayesian-statistics ├── 0_simple-bayesian-estimation.ipynb ├── 1_model-comparison-with-kombine.ipynb ├── README.md ├── assignment │ ├── README.md │ ├── answer-key.ipynb.gpg │ ├── assignment.md │ ├── data │ │ ├── Cag2008.csv │ │ └── Kor2005.csv │ └── rubric.md.gpg └── datasets │ └── hf-bond-energy.csv ├── bin ├── decrypt-answer └── encrypt-answer ├── course-info ├── README.md └── syllabus.md ├── environment.yml ├── envs ├── README.md ├── environment-osx.yml ├── environment-windows.yml └── img │ └── build-tools.png ├── generative-ai-molecules ├── 0_explain-policy-gradient-cartpole.ipynb ├── 1_explain-q-learning-cartpole.ipynb ├── README.md ├── environment.yml └── rlutils.py ├── ml-for-inorganic-materials ├── README.md └── environment.yml ├── molecular-property-prediction ├── README.md ├── chemoinformatics │ ├── 0_introduction-to-rdkit.ipynb │ ├── 1_ml-using-chemical-descriptors.ipynb │ ├── 2_ml-with-fingerprints.ipynb │ ├── README.md │ └── assignment │ │ ├── README.md │ │ ├── answer-key.ipynb.gpg │ │ ├── homework.md │ │ └── rubric.md.gpg ├── datasets │ └── qm9.json.gz ├── download-data.py ├── kernel-methods │ ├── README.md │ ├── assignment │ │ ├── README.md │ │ ├── answer-key.ipynb.gpg │ │ ├── assignment.md │ │ └── rubric.md.gpg │ ├── fchl-in-one-notebook.ipynb │ ├── figures │ │ ├── atomic-kernel.svg │ │ ├── fchl-scaling.svg │ │ ├── with-kernel.svg │ │ └── without-kernel.svg │ └── fitting-coulomb-matrix.ipynb └── message-passing-networks │ ├── 0_save-training-data.ipynb │ ├── 1_explain-data-loader.ipynb │ ├── 2_explain-message-passing-networks.ipynb │ ├── README.md │ ├── assignment │ ├── answer-key.ipynb.gpg │ ├── assignment.md │ └── rubric.md.gpg │ ├── datasets │ ├── test_data.proto │ ├── train_data.proto │ └── valid_data.proto │ └── mpnn │ ├── __init__.py │ ├── callbacks.py │ ├── data.py │ └── layers.py └── optimal-experimental-design ├── README.md ├── assignment ├── README.md ├── answer-key.ipynb.gpg ├── assignment.md ├── 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