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