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
Lectures and exercises
See Absalon when the course is set up.
The course requires a strong mathematical background. It is
suitable for computer science master students, as well as students
from mathematics (statistics, actuarial math, math-economics, etc)
and physics study programmes. Students from other study programmes
can verify if they have sufficient math and programming skills by
solving the self-preparation assignment (below) and if in doubt
contact the course organiser.
It is assumed that the students have successfully passed Machine Learning A (MLA) (or the Machine Learning (ML) course from previous years 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.
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, during course
- Type of assessment details
- A group project (written assignment), corresponding at 20 hours, developed during the course and documented with a report wherein the individual contributions are stated.
- Exam registration requirements
A prerequisite for taking the exam is the submission and approval of all but one of the 3 assignments. The submission dates for these assignments will be announced at the start of the course.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
Qualification for the re-examination is obtained by resubmission and approval of all but one of the 3 assignments no later than 3 weeks before the re-examination.
The re-exam consists of a 15-minute individual oral examinaion with no preparation time, based on the resubmission of the (possibly revised) final project and full syllabus. The revised project must be handed in no later than Wednesday before the re-examination week.
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Theory exercises
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Wouter Boomsma (2-846f4d71763b78823b7178)
Are you BA- or KA-student?
Courseinformation of students