Administrative information

Title	Tutorial: Inference and Generalisation
Duration	60
Module	A
Lesson Type	Tutorial
Focus	Technical - Foundations of AI
Topic	Foundations of AI

Keywords

inductive inference,Bayesian inference,naive Bayes,

Learning Goals

Learners understand the basic idea of inductive inference,
Learners understand the Bayesian treatment of inference and prediction,
Learners can train a Naive Bayesian classifier on real-world datasets.

Expected Preparation

Learning Events to be Completed Before

Lecture: Inference and Prediction

Obligatory for Students

A review of probability theory, especially Bayes' rule

Optional for Students

None.

References and background for students:

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

Recommended for Teachers

https://plato.stanford.edu/entries/logic-inductive/

Lesson Materials

Notebook

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

Instructions for Teachers

Prepare a Jupyter notebook environment with matplotlib, numpy, scipy and scikit-learn packages installed.

Outline/time schedule

Duration (min)	Description	Concepts
25	Introduction to Naive Bayesian methods	Bayes' rule, naive assumption, Bayesian inference, prediction
5	Generating toy data	Gaussian distribution, prior class probabilities, class conditional densities
10	Parameter inference and visualization	Multivariate Gaussian pdf, contour plots
10	Prediction and visualization	Posterior probabilities, argmax
10	GaussianNB on a real-world dataset	Evaluation of classifiers, accuracy

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