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
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
Lesson materials
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 |
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.