1. Course Structure
Assumptions with reasons and learning/reasoning.
Half half.
2. Basic Assumptions for Efficient Model Representation
- Independence: limit the number of interaction.
- Interaction: restrict the way things interact with each other.
2.1. Independence
2.2. Interaction
3. Additional Material
3.1. Sensitivityu and Specificity
- Sensitivity: True Positive
- Specificity: True Negtive
Simply another way saying the same thing.
敏感性(Sensitivity)与特异性(Specificity)
3.2. Bayes’ rule
$$
P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}
$$
$$
P(y=1|x=1) = \frac{P(x=1|y=1) \cdot P(y=1)}{P(x=1)}
$$
🍀后记🍀
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