Probabilistic Artificial Intelligence

DT8122 is a summer school course and, currently, only participants of the Nordic Probabilistic AI School (ProbAI) can register for the course.

The course is going to be organized in a series of lectures followed by hands-on tutorials. Also, there will be talks covering research and application areas related to the main topics. We will (tentatively) cover the following topics:

• Probabilistic models, variational inference and probabilistic programming
• Introduction to probabilistic modelling
• Bayesian modelling: prior, likelihood and posterior
• Concepts of Bayesian networks and latent-variable models
• Posterior inference and parameter learning
• Modelling techniques
• Variational inference:
  • Mean-field, CAVI and conjugate models
  • Stochastic Variational Inference and Optimization
  • Black-box variational inference
  • Automatic Differentiation
• Variational inference Probabilistic programming:
  • Introduction to the concept of probabilistic programming
  • Language syntax and semantics
  • Inference mechanisms
• Deep Generative Models
• Introduction to Deep Learning:
  • Examples of models
  • Stochastic optimization and backpropagation
  • Variational Auto-Encoders
• Bayesian Neural Networks Combining classical neural networks and probabilistic models
• Current applications of probabilistic AI techniques in data analysis

The main outcome of the course is to learn the principles of probabilistic models and deep generative models in Machine Learning and Artificial Intelligence, and acquiring skills for using existing tools that implement those principles (probabilistic programming languages).

Knowledge: The student will learn the theory of probabilistic modelling, variational inference, probabilistic programming and deep generative models.

Skills: Model designing, inference and programming with probabilistic models and deep generative models for a certain number of problems.

General competence: Thinking of machine learning and artificial intelligence problems and tasks from the principles of probabilistic modelling.

All teaching is done at the annual Probabilistic AI Summer School (https://probabilistic.ai). The only way to take the course is to be part of the summer-school the same year as you want to take the course.