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