Bayesian Machine Learning and Information Processing (5SSD0)

academic year 2023/24

The 2023/24 course “Bayesian Machine Learning and Information Processing” starts in November 2023 (Q2).

Course goals

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.

Course summary

This course covers the fundamentals of a Bayesian (i.e., probabilistic) approach to machine learning and information processing systems. The Bayesian approach allows for a unified and consistent treatment of 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 important 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 from interactions with their environment. These agents are used for applications such as self-driving cars or 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.

News and Announcements

  • (20-Dec-2023) There is an issue with viewing the lecture notes. Please see this Piazza note for a temporary solution.

  • (15-Nov-2023) 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.



In principle, you can download all needed materials from the links below.

Lecture Notes

The lecture notes are mandatory material for the exam:


The following books are optional but very useful for additional reading:


  • Please install Microsoft’s VS Code editor (download) and add the Jupyter notebook extension (tutorial).
  • Please install Julia version 1.9 (download) on your machine and then add the Julia extension in VS Code (tutorial).

Lecture notes, exercises, assignment and video recordings

You can access all lecture notes, videos and exercises online through the links below:

Date lesson materials
lecture notes exercises assignments video recordings
15-Nov-2023 B0: Course Syllabus
B1: Machine Learning Overview
B0, B1 B0, B1
17-Nov-2023 B2: Probability Theory Review B2 B2-ex
B2.1, B2.2
22-Nov-2023 B3: Bayesian Machine Learning B3 B3-ex
B3.1, B3.2
24-Nov-2023 B4: Factor Graphs and the Sum-Product Algorithm B4 B4-ex
B4.1, B4.2
27-Nov-2023 Deadline julia programming assignment A0
29-Nov-2023 B5: Continuous Data and the Gaussian Distribution B5 B5-ex
B5.1, B5.2
01-Dec-2023 B6: Discrete Data and the Multinomial Distribution B6 B6-ex
06-Dec-2023 Probabilistic Programming 1 - Bayesian inference with conjugate models W1 W1.1, W1.2
08-Dec-2023 B7: Regression B7 B7-ex
B7.1, B7.2
13-Dec-2023 B8: Generative Classification
B9: Discriminative Classification
B8, B9 B8-9-ex
B8, B9
15-Dec-2023 Probabilistic Programming 2 - Bayesian regression & classification W2 W2.1, W2.2
20-Dec-2023 B10: Latent Variable Models and Variational Bayes B10 B10-ex
B10.1, B10.2
Deadline probabilistic programming assignment 1 A1
10-Jan-2024 Probabilistic Programming 3 - Variational Bayesian inference W3 W3.1, W3.2
12-Jan-2024 B11: Dynamic Models B11 B11-ex
17-Jan-2024 Probabilistic Programming 4 - Bayesian filters & smoothers W4 W4.1, W4.2
19-Jan-2024 B12: Intelligent Agents and Active Inference B12 B12-ex
B12.1, B12.2
26-Jan-2024 Deadline probabilistic programming assignment 2 A2
01-Feb-2024 written examination (13:30-16:30)
19-Apr-2024 resit written examination (18:00-21:00)

Exams & Assignments


  • Consult the Course Syllabus (lecture notes for 1st class) for advice on how to study the materials.

programming assignments

  • Programming assignments can be downloaded from the table above.
  • Programming assignments should be submitted before the indicated deadlines at the Canvas Assignments tab.



  • If you liked this class, here is a short oversight (~10 minutes) of internship and graduation projects that you may consider applying for.