Administrative information

Title	Inference and Prediction
Duration	60
Module	A
Lesson Type	Lecture
Focus	Technical - Foundations of AI
Topic	Foundations of AI

Keywords

Bayesian inference, maximum likelihood, maximum a posteriori, Bayesian model averaging.,

Learning Goals

Learners understand the basic idea of Bayesian thinking,
Learners are familiar with ML and MAP inference with various distributions,
Learners understand the algorithmic aspects of ML/MAP inference and prediction,
Learners understand the idea of Bayesian model averaging and probabilistic predictions.

Expected Preparation

Learning Events to be Completed Before

None.

Obligatory for Students

Review of basic probability theory.

Optional for Students

None.

References and background for students:

Bishop, Christopher M. (2006). Pattern recognition and machine learning, Chapter 1 and 2. For a brief review of probability theory, see Section 1.2.

Recommended for Teachers

Familiarize themselves with the demonstration materials.

Lesson Materials

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

Instructions for Teachers

Cover the topics in the lesson outline and demonstrate the concepts using the interactive notebooks (likelihood maximization/loss minimization, relationship between the prior, posterior and the number of observations). Give a brief overview of the code.

Outline/time schedule

Duration (min)	Description	Concepts
10	Bayesian treatment of a coin toss	observation, parameter, Bernoulli distribution
10	Inference via maximum likelihood	likelihood, loss function, crossentropy
10	Demonstration (likelihood maximization)	-
15	Probabilistic inference via Bayes' theorem	prior, posterior, Beta distribution, hyperparameters, maximum a posteriori
5	Demonstration (prior and posterior)	-
10	Predictive distribution and model averaging	predictive distribution, Bayesian model averaging

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