• Reading:
  • AIMA: Sections 12.4–12.6 and 13.1–13.3. Focus on conditional independence, Naive Bayes, Bayesian-network semantics, evidence flow, and exact inference by variable elimination. Additionally, skim Section 21.2 for the final notebook segment on estimating probability tables from data.
• Technical preparation:
  • Open today's notebook and run all setup cells and the first code cell in Segment 1 to verify that the notebook works in your environment. The output should display the posterior probability distribution over Flu and No flu after observing both fever and cough.

• 08:30 – MS Forms
• 10:00 – MS Forms

1. Can more evidence make a model less reliable?
2. Why can confirming one cause make another cause less likely?
3. Why does variable elimination order matter if the posterior remains the same?
4. Can a correctly fitted Bayesian network still give misleading probabilities?

Notebook: HCAI_Lab_Uncertainty.ipynb (Work in Google Colab or download the .ipynb file to local environment)

Work in groups of 2-4. Every group member must be able to explain the results.