Probabilistic Machine Learning (PML)

Uncertainty is a central concept in many areas of Science and Society, yet it is often neglected in Machine Learning. This course demonstrates how the probabilistic framework gives us a powerful language to describe uncertainties about both models and predictions. We will cover a range of different probabilistic modelling techniques, and demonstrate the impact of uncertainty quantification on real-world data. Finally, we will demonstrate how model design and inference can be cleanly separated using modern probabilistic programming languages, making it possible to express complex models in a modular and concise form.

This is an advanced topics course, and the exact list of topics will therefore change from year to year, depending on current trends in the literature. Examples of topics include:

• Fundamental concepts. What is a probability? Frequentist vs Bayesian perspectives.
• Inference techniques: Markov chain Monte Carlo, Variational Inference, and advanced methods
• Uncertainty quantification and probability calibration
• Latent variable models: Mixtures, Deep latent variable models
• Graphical models
• Gaussian Processes, Bayesian optimization
• Flow models
• Bayesian decision theory
• Probabilistic Programming fundamentals
• Probabilistic Programming Language design

WARNING:
If you have not taken DIKU's Machine Learning master course, please, carefully check the "Recommended Academic Qualifications" box below and the self-preparation assignment at
  http://https:/​/​sites.google.com/​diku.edu/​machine-learning-courses/pml.
 Machine Learning courses given at other places do not necessarily prepare you well for this course. It is not advised to take the course if you do not meet the academic qualifications.