applied machine

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1. 1▶HTE: Confounding-Robust ForestsMachine Learning & Causal Inference: A Short CourseNotes
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2. 2▶HTE: Confounding-Robust EstimationMachine Learning & Causal Inference: A Short CourseNotes
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3. 3▶HTE: Sources of BiasMachine Learning & Causal Inference: A Short CourseNotes
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4. 4▶Robust Estimation of Treatment HeterogeneityMachine Learning & Causal Inference: A Short CourseNotes
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5. 5▶Conditional Average Treatment Effects: ForestsMachine Learning & Causal Inference: A Short CourseNotes
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6. 6▶Conditional Average Treatment Effects: TreesMachine Learning & Causal Inference: A Short CourseNotes
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7. 7▶Conditional Average Treatment Effects: OverviewMachine Learning & Causal Inference: A Short CourseNotes
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8. 8▶Average Treatment Effects: Double RobustnessMachine Learning & Causal Inference: A Short CourseNotes
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9. 9▶Average Treatment Effects: Propensity ScoresMachine Learning & Causal Inference: A Short CourseNotes
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10. 10▶Average Treatment Effects: ConfoundingMachine Learning & Causal Inference: A Short CourseNotes
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11. 11▶Channel Intro - Applied Machine LearningApplied Machine Learning 2020Notes
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12. 12▶Applied ML 2020 - 14 - Clustering and Mixture ModelsApplied Machine Learning 2020Notes
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13. 13▶Applied ML 2020 - 13 - Dimensionality reductionApplied Machine Learning 2020Notes
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14. 14▶Applied ML 2020 - 12 - AutoML (plus some feature selection)Applied Machine Learning 2020Notes
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15. 15▶Applied ML 2020 - 11 - Model Inspection and Feature SelectionApplied Machine Learning 2020Notes
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16. 16▶Applied ML 2020 - 10 - Calibration, Imbalanced dataApplied Machine Learning 2020Notes
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17. 17▶Applied ML 2020 - 09 - Model Evaluation and MetricsApplied Machine Learning 2020Notes
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18. 18▶Applied ML 2020 - 08 - Gradient BoostingApplied Machine Learning 2020Notes
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19. 19▶Applied ML 2020 - 07 - Decision Trees and Random ForestsApplied Machine Learning 2020Notes
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20. 20▶Applied ML 2020 - 06 - Linear Models for ClassificationApplied Machine Learning 2020Notes
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21. 21▶Applied Machine Learning 2019 - Lecture 15 - Clustering and Mixture modelsApplied Machine Learning - Spring 2019Notes
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22. 22▶Applied Machine Learning 2019 - Lecture 14 - Dimensionality ReductionApplied Machine Learning - Spring 2019Notes
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23. 23▶Applied Machine Learning 2019 - Lecture 13 - Parameter Selection and Automatic Machine LearningApplied Machine Learning - Spring 2019Notes
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24. 24▶Applied Machine Learning 2019 - Lecture 12 - Model Interpretration and Feature SelectionApplied Machine Learning - Spring 2019Notes
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25. 25▶Applied Machine Learning 2019 - Lecture 11 - Imbalanced dataApplied Machine Learning - Spring 2019Notes
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26. 26▶Applied Machine Learning 2019 - Lecture 10 - Model EvaluationApplied Machine Learning - Spring 2019Notes
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27. 27▶Applied Machine Learning 2019 - Lecture 09 - Gradient boosting; CalibrationApplied Machine Learning - Spring 2019Notes
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28. 28▶Applied Machine Learning 2019 - Lecture 08 - Trees, Forests and EnsemblesApplied Machine Learning - Spring 2019Notes
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29. 29▶Applied Machine Learning 2019 - Lecture 07 - Linear Models for Classifications, SVMsApplied Machine Learning - Spring 2019Notes
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30. 30▶Applied Machine Learning 2019 - Lecture 06 - Linear Models for RegressionApplied Machine Learning - Spring 2019Notes
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