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
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 |
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.