机器学习和人工智能基础预测、因果关系和统计推断
- 0 - Introduction
- 1. Prediction, causation, and statistical inference
- 1 - What Is a Casual Model
- 1. Lady tasting tea
- 2. Why causation matters in a business setting
- 3. What is a causal model
- 2 - Healthy Skepticism about Our Data and Our Results
- 1. Skepticism about data Truman 1948 Election Poll
- 2. Skepticism about results Is that really the best predictor
- 3. Skepticism about causes Is X really causing Y
- 3 - Correlation Does Not Imply Causation
- 1. What is a strong correlation
- 2. Pearson on correlation and causation
- 3. Correlation and regression
- 4. Challenge What is causing what
- 5. Solution What is causing what
- 4 - Prediction and Proof in Statistics
- 1. Using probability to measure uncertainty
- 2. p-value review
- 3. Hypothesis testing checklist
- 4. Taleb on normality, mediocristan, and extremistan
- 5. Challenge Evaluate significant finding
- 6. Solution Evaluate significant finding
- 5 - Deduction and Induction
- 1. What are induction and deduction
- 2. Hume on induction
- 3. Popper on induction and falsification
- 4. Taleb on induction
- 5. Counterfactuals Pearl on induction and causality
- 6 - Prediction and Proof in Data Mining
- 1. Data mining vs. data dredging
- 2. TrainTest What can go wrong
- 3. AB testing during the evaluation phase
- 7 - The Two Cultures Contrasting Statistics and Data Mining
- 1. The Two Cultures
- 2. Explain vs. predict
- 3. Comparing CRISP-DM and the scientific method
- 4. Applying the two methods at work
- 8 - Conclusion
- 1. Review