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Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference

Mar 18, 2022 • Keith McCormick

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About this course

Gain insights to help improve your machine learning models and statistical analyses.



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Prediction, causation, and statistical inference

1m 27s
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Lady tasting tea

4m 48s
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Why causation matters in a business setting

1m 27s
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What is a causal model?

2m 13s
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Skepticism about data: Truman 1948 Election Poll

3m 7s
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Skepticism about results: Is that really the best predictor?

3m 47s
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Skepticism about causes: Is X really causing Y?

2m 59s
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What is a strong correlation?

6m 53s
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Pearson on correlation and causation

5m 1s
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Correlation and regression

5m 6s
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Challenge: What is causing what?

1m 53s
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Solution: What is causing what?

7m 9s
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Using probability to measure uncertainty

8m 23s
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p-value review

1m 28s
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Hypothesis testing checklist

3m 59s
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Taleb on normality, mediocristan, and extremistan

2m 39s
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Challenge: Evaluate significant finding

1m 50s
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Solution: Evaluate significant finding

5m 34s
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What are induction and deduction?

4m 21s
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Hume on induction

3m 35s
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Popper on induction and falsification

4m 32s
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Taleb on induction

4m 24s
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Counterfactuals: Pearl on induction and causality

2m 24s
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Data mining vs. data dredging

5m 49s
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Train/Test: What can go wrong?

4m 57s
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A/B testing during the evaluation phase

2m 45s
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The Two Cultures

5m 6s
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Explain vs. predict

5m 4s
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Comparing CRISP-DM and the scientific method

4m 35s
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Applying the two methods at work

4m 11s
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Review

1m 4s