- Probability in AI
- Dependence
- Bayes Rule
- Bayes Network (influence diagram)
- Conditional Independence
- Conditional Dependence
- Probabilistic Inference
- Causal Direction
- Variable Elimination
- Rejection Sampling
- Gibbs Sampling
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Bayes Network |
The big question: "can we diagnose the problem?" This sort of diagnostic thinking introduced with the influence diagram of car-not-starting problem is a very useful thing for a maker/hacker. Reasoning from observables to hidden causes. Probabilities are the basic tool for managing uncertainty in a wide variety of different AI fields (e.g. vision, robotics, machine learning).
I haven't done any programming yet to solve the quizzes or homeworks; I used a spreadsheet to do the simple probability calculations in this lesson (you could easily do them with pencil and paper).
Lol I have only watched the very first video. I am so behind, I doubt I will be able to ever catch up. They are about to put me on tons of overtime at work also.
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