Content of Learning Activity

This course deals with the field at the intersection of artificial intelligence and probability theory: probabilistic graphical models, Bayesian networks, and their implementation through probabilistic programming. Decision-making in the face of uncertainty (missing information, noisy data, etc.) by statistical inference constitutes one of the major contributions of probabilistic AI. This course will offer the following content:
- Theoretical reminders on the theory of probabilities,
- General theory of probabilistic graphical models and Bayesian networks,
- Theory of commonly used models: HMMs (hidden Markov models), particle filters, unsupervised grouping methods (clustering with continuous and discrete variables),
- Learning / inference methods with these models: inference and learning with so-called hidden variables (not measurable but important for classification or decision-making), Monte-Carlo simulation, Expectation-Maximization (EM) algorithm, IA and generative models.

This course will include practicals to acquire the theory.
 

Required Learning Resources/Tools

All learning resources and tools required for this cours are available via Moodle, the online e-learning platform of UMONS.

Recommended Learning Resources/Tools

Additional recommended material is also accessible through Moodle, the online e-learning platform of UMONS.

Other Recommended Reading

Not applicable

Mode of delivery

• Face to face
• From a distance
• Mixed

Type of Teaching Activity/Activities

• Cours magistraux
• Travaux pratiques
• Travaux de laboratoire
• Projet sur ordinateur

Evaluations

The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)