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Issues list

Module 2 - Lesson 10: Consolidate what you have learned, and explore machine learning Lesson Lesson outcomes and outline
#19 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 9: Liquid modernity, multiple jurisdictions, and assessing causality in randomised control trials Lesson Lesson outcomes and outline
#18 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 8: Human agency and autonomous systems, and permutation testing for classification Lesson Lesson outcomes and outline
#17 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 7: Counterfactual consequences, and implementing, testing and optimising classifiers Lesson Lesson outcomes and outline
#16 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 6: Emergent systems, strange loops, and supervised and unsupervised learning techniques Lesson Lesson outcomes and outline
#15 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 5: Strong and weak machine intelligence, and classification using logistic regression Lesson Lesson outcomes and outline
#14 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 4: Ultimatum games, “fairness” and model selection for multiple regression Lesson Lesson outcomes and outline
#13 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 3: Reflective equilibrium, and methods for multiple regression Lesson Lesson outcomes and outline
#12 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 2: Doctrine of double effect, and interpreting regression with visual and numerical diagnostics Lesson Lesson outcomes and outline
#11 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 2 - Lesson 1: Trolley problems, and predictions using regression and least squares Lesson Lesson outcomes and outline
#10 opened Sep 16, 2019 by turukawa Module 2
@turukawa
Module 1 - Lesson 10: Publishing and evaluating studies based on cohort data and analysis of variance Lesson Lesson outcomes and outline
#9 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 9: Sample robustness, central limit theory, and the ethics and abuses of p-hacking Lesson Lesson outcomes and outline
#8 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 8: Bootstrapping and the risks of algorithmic decision-making Lesson Lesson outcomes and outline
#7 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 7: Hypothesis testing, and risks for policy from poor data Lesson Lesson outcomes and outline
#6 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 6: Techniques in data and population sampling, and assessing standard error Lesson Lesson outcomes and outline
#5 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 5: Expected statistical outcomes using distributions, and issues for analysis Lesson Lesson outcomes and outline
#4 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 4: Sampling, data distribution, and secure data custody Lesson Lesson outcomes and outline
#3 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 3: Probability, randomness, and the risk of de-anonymization Lesson Lesson outcomes and outline
#2 opened Sep 16, 2019 by turukawa Module 1
@turukawa
Module 1 - Lesson 2: Research and experiments with data Lesson Lesson outcomes and outline
#1 opened Sep 16, 2019 by turukawa Module 1
@turukawa
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