Administrative information

Title	Decision theory
Duration	60
Module	A
Lesson Type	Tutorial
Focus	Tecnical - 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

Lecture: Decision Theory

Obligatory for Students

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

Notebook for decision theory

The materials of this learning event are available under CC BY-NC-SA 4.0.

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
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

More information

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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