Administrative Information
| Title | Decision theory |
| Duration | 60 |
| Module | A |
| Lesson Type | Tutorial |
| Focus | Technical - Foundations of AI |
| Topic | Foundations of AI |
Keywords
Bayes' theorem,maximum expected utility,optimal decision,Bayes classifier,Bayes error rate,
Learning Goals
- Bayes' theorem
- maximum expected utility
- optimal decision
- Bayes classifier
- Bayes error rate
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Python and pandas skills
- Naive Bayes Classifier
- Logistic regression
- Influence diagram
Optional for Students
- Artificial Intelligence: A Modern Approach, 4th Global ed. by Stuart Russell and Peter Norvig, Pearson (AIMA4e):ch12-18
References and background for students
- AIMA4e:ch12-18
Recommended for Teachers
- Domingos, P. and Pazzani, M., 1997. On the optimality of the simple Bayesian classifier under zero-one loss. Machine learning, 29(2), pp.103-130.
- AIMA4e:ch12-18
Lesson materials
Instructions for Teachers
- Adapt NBN model
- Generate synthetic data with various size (and optionally incompletness/noise levels)
- Explore decision regions
- During in-class activities, ask students to select cases
Outline/time schedule
| Duration | Description | Concepts | Activity | Material |
|---|---|---|---|---|
| 15 | Global and local risk, decision regions, Bayes error | risk | ||
| 15 | Generative versus predictive models: logistic regression vs. NBN | logistic regression | ||
| 15 | Learning of the Naive Bayes net from data | Naive Bayes net | ||
| 15 | Learning of a logistic regression from data | logistic regression |
Acknowledgements
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.