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
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(20-Dec-2023) There is an issue with viewing the lecture notes. Please see this Piazza note for a temporary solution.
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(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.
Instructors
- Prof.dr.ir. Bert de Vries (email: bert.de.vries@tue.nl) is the responsible instructor for this course and teaches the lectures with label B.
- Dr. Wouter Kouw (w.m.kouw@tue.nl) teaches the probabilistic programming lectures with label W.
- Tim Nisslbeck, Sepideh Adamiat and Wouter Nuijten are the teaching assistants.
Materials
In principle, you can download all needed materials from the links below.
Lecture Notes
The lecture notes are mandatory material for the exam:
- Bert de Vries (2023), PDF bundle of all lecture notes for lessons B0 through B12.
- Wouter Kouw (2023), PDF bundle of all probabilistic programming lecture notes for lessons W1 through W4.
Books
The following books are optional but very useful for additional reading:
- Christopher M. Bishop (2006), Pattern Recognition and Machine Learning. You can also buy a hardcopy, e.g. at bol.com.
- Ariel Caticha (2012), Entropic Inference and the Foundations of Physics.
Software
- 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-sol |
B2.1, B2.2 | |
| 22-Nov-2023 | B3: Bayesian Machine Learning | B3 | B3-ex B3-sol |
B3.1, B3.2 | |
| 24-Nov-2023 | B4: Factor Graphs and the Sum-Product Algorithm | B4 | B4-ex B4-sol |
B4.1, B4.2 | |
| 27-Nov-2023 | Deadline julia programming assignment | A0 A0-sol |
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| 29-Nov-2023 | B5: Continuous Data and the Gaussian Distribution | B5 | B5-ex B5-sol |
B5.1, B5.2 | |
| 01-Dec-2023 | B6: Discrete Data and the Multinomial Distribution | B6 | B6-ex B6-sol |
B6 | |
| 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-sol |
B7.1, B7.2 | |
| 13-Dec-2023 | B8: Generative Classification B9: Discriminative Classification |
B8, B9 | B8-9-ex B8-9-sol |
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-sol |
B10.1, B10.2 | |
22-Dec-2023 |
Deadline probabilistic programming assignment 1 | A1 A1-sol |
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| break | |||||
| 10-Jan-2024 | Probabilistic Programming 3 - Variational Bayesian inference | W3 | W3.1, W3.2 | ||
| 12-Jan-2024 | B11: Dynamic Models | B11 | B11-ex B11-sol |
B11 | |
| 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-sol |
B12.1, B12.2 slides |
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| 26-Jan-2024 | Deadline probabilistic programming assignment 2 | A2 A2-sol |
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| 01-Feb-2024 | written examination (13:30-16:30) | ||||
| 19-Apr-2024 | resit written examination (18:00-21:00) | ||||
Exams & Assignments
Preparation
- Consult the Course Syllabus (lecture notes for 1st class) for advice on how to study the materials.
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In addition to the materials in the above table, we provide two representative practice written exams:
- 2021-01-18: exam A, solutions A; exam B, solutions B
- 2021-04-15: exam, solutions
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.
grading
Projects
- If you liked this class, here is a short oversight (~10 minutes) of internship and graduation projects that you may consider applying for.