机器学习和人工智能基础因果推理和建模
上次更新时间：2024-09-10
课程售价: 2.9 元
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课程内容
0 - Introduction
1 - Experimental Design and Statistical Controls
- 1. The investigator, the jury, and the judge
- 2. Fisher and experiments
- 3. John Snow and natural experiments
- 4. Double blind studies
- 5. Control variables (ANCOVA)
- 6. Judea Pearl Problems with control variables
- 7. Moderation, mediation, and lurking variables
- 8. Simpson's paradox
- 9. Challenge Moderation, mediation, or a third variable
- 10. Solution Moderation, mediation, or a third variable
2 - Conditional Probability and Bayes' Theorem
- 1. Turing, Enigma, and CAPTCHA
- 2. Enigma and uncertainty
- 3. Developing an intuition for Bayes with Wordle
- 4. Wordle and conditional probability
- 5. Wordle, bans, and bits
- 6. Wordle and Bayes' theorem
- 7. Challenge Conditional probability and Bayes' theorem
- 8. Solution Conditional probability and Bayes' theorem
3 - Prediction and Proof with Bayesian statistics
4 - Causal Modeling with Structural Equation Modeling (SEM)
5 - Causal Modeling with Bayesian Networks
6 - Conclusion
课程内容
7个章节 , 38个讲座
0 - Introduction
1 - Experimental Design and Statistical Controls
- 1. The investigator, the jury, and the judge
- 2. Fisher and experiments
- 3. John Snow and natural experiments
- 4. Double blind studies
- 5. Control variables (ANCOVA)
- 6. Judea Pearl Problems with control variables
- 7. Moderation, mediation, and lurking variables
- 8. Simpson's paradox
- 9. Challenge Moderation, mediation, or a third variable
- 10. Solution Moderation, mediation, or a third variable
2 - Conditional Probability and Bayes' Theorem
- 1. Turing, Enigma, and CAPTCHA
- 2. Enigma and uncertainty
- 3. Developing an intuition for Bayes with Wordle
- 4. Wordle and conditional probability
- 5. Wordle, bans, and bits
- 6. Wordle and Bayes' theorem
- 7. Challenge Conditional probability and Bayes' theorem
- 8. Solution Conditional probability and Bayes' theorem