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
The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
