机器学习和人工智能基础预测、因果关系和统计推断

上次更新时间：2024-09-10

课程售价: 2.9 元

联系右侧微信客服充值或购买课程

课程内容

0 - Introduction

1. Prediction, causation, and statistical inference (免费)

1 - What Is a Casual Model

1. Lady tasting tea (免费)
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

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机器学习和人工智能基础预测、因果关系和统计推断

上次更新时间：2024-09-10 18:59

课程售价：2.9元

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课程内容

9个章节 , 31个讲座

0 - Introduction

1. Prediction, causation, and statistical inference (免费)

1 - What Is a Casual Model

1. Lady tasting tea (免费)
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