Issues · whythawk/data-as-a-science · GitHub

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Module 2 - Lesson 10: Consolidate what you have learned, and explore machine learning

#19

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 9: Liquid modernity, multiple jurisdictions, and assessing causality in randomised control trials

#18

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 8: Human agency and autonomous systems, and permutation testing for classification

#17

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 7: Counterfactual consequences, and implementing, testing and optimising classifiers

#16

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 6: Emergent systems, strange loops, and supervised and unsupervised learning techniques

#15

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 5: Strong and weak machine intelligence, and classification using logistic regression

#14

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 4: Ultimatum games, “fairness” and model selection for multiple regression

#13

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 3: Reflective equilibrium, and methods for multiple regression

#12

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 2: Doctrine of double effect, and interpreting regression with visual and numerical diagnostics

#11

· turukawa opened

on Sep 16, 2019

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Module 2 - Lesson 1: Trolley problems, and predictions using regression and least squares

#10

· turukawa opened

on Sep 16, 2019

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Module 1 - Lesson 10: Publishing and evaluating studies based on cohort data and analysis of variance

#9

· turukawa opened

on Sep 16, 2019

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Module 1 - Lesson 9: Sample robustness, central limit theory, and the ethics and abuses of p-hacking

#8

· turukawa opened

on Sep 16, 2019

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