Administrative information
| Title |
Decision networks |
 |
| Duration |
60 |
| Module |
A |
| Lesson Type |
Lecture |
| Focus |
Technical - Foundations of AI |
| Topic |
Foundations of AI |
Keywords
Naive Bayesian networks,Bayesian networks,Decision networks,maximum exp utility principle,optimal decision,probabilistic inference,value of information,
Learning Goals
- Naive Bayesian networks
- Bayesian networks
- Decision networks
- Students can define a multivariate joint distribution: multinomial and Gaussian
- Students can explain the difference between association versus causation
- Students can define observational, causal, and counterfactual inference
- Students can define fairness using observational and counterfactual reasoning
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Probability (e.g. from AIMA4e or wikipedia)
- basic concepts of probability theory
- multivariate joint probability distributions, chain rule
Optional for Students
- Artificial Intelligence: A Modern Approach, 4th Global ed. by Stuart Russell and Peter Norvig, Pearson (AIMA4e):ch16-17
References and background for students:
Recommended for Teachers
- AIMA4e:ch16-17
- Charniak, E., 1991. Bayesian networks without tears. AI magazine, 12(4), pp.50-50.
- Pearl, J., 2019. The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3), pp.54-60.
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
- Reminder: framework of a one-step decision problem, elements (action, uncertainty, utility/loss), maximum expected utility principle
- Reminder: probabilistic graphical models, causal diagrams
- Define elements of a decision network: chance, action, utility/ loss nodes
- Explain workflow: evidences, actions, probabilistic inference, expectations, maximizing action
- Example
- Discuss value of information
Outline/time schedule
| Duration |
Description |
| 10 |
Multivariate joint distribution: multinomial and Gaussian |
| 5 |
Difference between association versus causation |
| 15 |
General Bayesian networks |
| 15 |
Observational, causal, and counterfactual inference |
| 15 |
Example: definition of fairness using observational, causal and counterfactual reasoning |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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