Bayesian Machine Learning and Information Processing (5SSD0)

academic year 2025/26

Note: This site is currently under construction.

The course “Bayesian Machine Learning and Information Processing” (5SSD0) starts in November 2025 (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

Instructors

Materials

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

Lecture Notes

The lecture notes are mandatory material for the exam:

Books

The following book is optional but very useful for additional reading:

Software

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

Lecture notes, exercises, assignment and video recordings

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

Date lesson materials
lecture notes exercises assignments video recordings
12-Nov-2025
(Wednesday)
B0: Course Syllabus
B1: Machine Learning Overview
B0, B1 B0, B1
14-Nov-2025 (Fri) B2: Probability Theory Review B2 B2-ex
B2-sol
B2.1, B2.2
19-Nov-2025 (Wed) B3: Bayesian Machine Learning B3 B3-ex
B3-sol
B3.1, B3.2
21-Nov-2025 (Fri) B4: Factor Graphs and the Sum-Product Algorithm B4 B4-ex
B4-sol
B4.1, B4.2
26-Nov-2025 (Wed) Introduction to Julia W0
28-Nov-2025 (Fri) Pick-up Julia programming assignment A0 A0
28-Nov-2025 (Fri) B5: Continuous Data and the Gaussian Distribution B5 B5-ex
B5-sol
B5.1, B5.2
03-Dec-2025 (Wed) B6: Discrete Data and the Multinomial Distribution B6 B6-ex
B6-sol
B6
05-Dec-2025 (Fri) Probabilistic Programming 1 - Bayesian inference with conjugate models W1 W1.1, W1.2
05-Dec-2025 Submission deadline assignment A0 submit
05-Dec-2025 Pick-up probabilistic programming assignment A1 A1
10-Dec-2025 (Wed) B7: Regression B7 B7-ex
B7-sol
B7.1, B7.2
12-Dec-2025 (Fri) B8: Generative Classification
B9: Discriminative Classification
B8, B9 B8-9-ex
B8-9-sol
B8, B9
17-Dec-2025 (Wed) Probabilistic Programming 2 - Bayesian regression & classification W2 W2.1, W2.2
19-Dec-2025 (Fri) B10: Latent Variable Models and Variational Bayes B10 B10-ex
B10-sol
B10.1, B10.2
19-Dec-2025 Submission deadline assignment A1 submit
break
07-Jan-2026 (Wed) Probabilistic Programming 3 - Variational Bayesian inference W3 W3.1, W3.2
09-Jan-2026 (Fri) B11: Dynamic Models B11 B11-ex
B11-sol
B11
09-Jan-2026 Pick-up probabilistic programming assignment A2 A2
14-Jan-2026 (Wed) B12: Intelligent Agents and Active Inference B12,
slides
B12-ex
B12-sol
B12.1, B12.2
16-Jan-2026 (Fri) Probabilistic Programming 4 - Bayesian filters & smoothers W4 W4.1, W4.2
23-Jan-2026 (Fri) Submission deadline assignment A2 submit
29-Jan-2026 (Thu) written examination (13:30-16:30)
- Pick-up resit programming assignment download
- Submission deadline resit assignment submit
- resit written examination (18:00-21:00)

Exams & Assignments

Exam Rules

  • You can not bring a formula sheet, nor use a phone or calculator at the exam. Any needed formulas are supplied in the pre-amble of the exam.

Exam Preparation

  • 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.

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

  • When doing exercises from the above table, feel free to make use of Sam Roweis’ cheat sheets for Matrix identities and Gaussian identities.

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

Programming Assignments

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

Grading

  • 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.

Resit

Instructors