This course provides an introduction to Bayesian machine learning and information processing systems. The Bayesian approach affords a unified and consistent treatment of many useful information processing systems.

This course covers the fundamentals of a Bayesian (i.e., probabilistic) approach to machine learning and information processing systems. The Bayesian approach provides a unified, consistent framework for many model-based machine learning techniques.

Initially, we focus on Linear Gaussian systems and will discuss many useful models and applications, including common regression and classification methods, Gaussian mixture models, hidden Markov models, and Kalman filters. We will discuss essential algorithms for parameter estimation in these models, including the Variational Bayes method.

The Bayesian method also provides tools for comparing the performance of different information processing systems by means of estimating the Bayesian evidence for each model. We will discuss several methods for approximating Bayesian evidence.

Next, we will discuss intelligent agents that learn purposeful behavior through interactions with their environment. These agents are used in applications such as self-driving cars and the interactive design of virtual and augmented realities.

Indeed, in this course, we relate synthetic Bayesian intelligent agents to natural intelligent agents such as the brain. You will be challenged to code Bayesian machine learning algorithms yourself and apply them to practical information processing problems.

• (12-Nov-2025) Please sign up for Piazza (Q&A platform) at signup link. As much as possible, we will use the Piazza site for new announcements as well.

• Prof.dr.ir. Bert de Vries (email: [email protected]) is the responsible instructor for this course and teaches the lectures with label B.
• Dr. Wouter Kouw ([email protected]) teaches the probabilistic programming lectures with label W.
• Fons van der Plas is our educational advisor who is responsible for all Pluto-related issues. If you have ideas on making the course more interactive, contact Fons.
• Wouter Nuijten and Thijs Jenneskens are the teaching assistants.

All course materials are available in the table below. If necessary, you can download the lecture notes in PDF format here:

• B lecture notes version 10-Nov-2025
• W lecture notes version 10-Nov-2025

We recommend that you read the lecture notes in your browser to take advantage of the interactive materials that we prepared for this course, based on Pluto.jl.

The following (freely downloadable) book is optional but very useful for additional reading:

• Christopher M. Bishop (2006), Pattern Recognition and Machine Learning.

Please follow the software installation instructions. If you encounter any problems, please get in touch with us in class or on Piazza.

You can access all lecture materials online through the links below:

• You can not bring a formula sheet, nor use a phone or calculator at the exam. This Formula Sheet will be provided in the preamble of the exam. You can use the formula sheet when making any exercises.

• The written exam will be a multiple-choice exam, just like the examples below. This year, there will be no probabilistic programming question in the written exam.

• In addition to the materials in the above table, we provide two representative practice written exams:

  • 3-Feb-2022: exam ; answers ; calculations
  • 2-Feb-2023: exam ; answers ; calculations

• Programming assignments can be downloaded and submitted through the links in the above table.

• The final grade is composed of the results of assignments A1 (10%), A2 (10%), and the final written exam (80%). The grade will be rounded to the nearest integer.

For instructors:

Take a look at https://github.com/bmlip/course/tree/main/developer%20instructions for more information aimed at the course lecturers and website admins.