NDAK21004U 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
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
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.
After completing the course, the student will have:
- fundamental concepts in probabilistic machine learning
- the trade-offs between different inference techniques
- common probabilistic models
- fundamental concepts in probabilistic programming
- implementing different probabilistic models, with and without the use of a probabilistic programming language.
- quantifying and calibrating uncertanties
- assessing model quality (including convergence criteria and
appropriateness of variational distributions)
- analyzing problems and formulating appropriate probabilistic models
- identifying strengths and weaknesses of different models and modelling approaches
- solving modelling projects in collaboration with others
See Absalon when the course is set up.
It is assumed that the students have successfully passed the “Machine Learning” course offered by the Department of Computer Science (DIKU). In case you have not taken the “Machine Learning” course at DIKU, please, go through the self-preparation material and solve the self-preparation assignment provided at https://sites.google.com/diku.edu/machine-learning-courses/pml before the course starts. (For students with a strong mathematical background and some background in machine learning it should be possible to do the self-preparation within a couple of weeks.) It is strongly advised not to take the course if you do not meet the prerequisites.
The working language of the course is Python. All our examples and help are provided in Python and it is recommended to be familiar with Python before starting the course.
- Theory exercises
- Practical exercises
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- Written assignment, 20 h during courseA group project (written assignment), corresponding at 20 hours, developed during the course and documented with a report wherein the individual contributions are stated.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
As the ordinary exam.
The project and the report can be revised and resubmitted.
Criteria for exam assesment
See Learning Outcome.