Nordic Probabilistic AI School

13 Jun 2022 - 17 Jun 2022

The ProbAI school is here to provide an inclusive education environment serving state-of-the-art expertise in machine learning and artificial intelligence.

More info & registration ⇗

Practical information:

13 Jun 2022 - 17 Jun 2022

Helsinki

English

Target audience: Everyone: PhD students, exceptionally advanced MSc or BSc students, experienced researchers (postdocs, faculty or industry members), engineers and other practitioners from industry, and also hobbyists

Want to register?

Register until: 27 Mar 2022
Price: Students (including PhD) → 250 EUR; Academia → 500 EUR; Industry → 1000 EUR

More info & registration ⇗

Georganiseerd door:

Objective

Our objective is to bring an intermediate to advanced level “summer” school with a focus on probabilistic machine learning. We cover topics such as probabilistic models, variational approximations, deep generative models, latent variable models, normalizing flows, neural ODEs, probabilistic programming, and much more.

More info

Concept

We intend to provide an efficient and quality knowledge transfer through:

carefully designed curriculum and program,
coordination between our lecturers,
a mix of theoretical lectures and hands-on tutorials,
support for students from our teaching assistants,
fostering close interaction and collaboration between students, lecturers and teaching assistants.

All that in a friendly and inclusive environment that we will co-create, together. Read our Code of Conduct.

Topics

The above program will be structured into theoretical lectures and hands-on tutorials with the following modules:

Probabilistic models, variational inference and probabilistic programming (Day 1 to Day 2)
- Introduction to probabilistic modeling
  - Bayesian modeling: prior, likelihood and posterior
  - Concepts of Bayesian networks and latent-variable models
  - Posterior inference and parameter learning
  - Modeling 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 (Day 3 to Day 5)
- Variational Auto-Encoders
- Generative Adversarial Networks
- Normalizing Flows
- ODEs and Bayesian Neural Nets
- Simulation-Based Inference
- Bayesian Neural Networks
- Gaussian Processes