Note: This site is currently under construction.
The 2021/22 course “Bayesian Machine Learning and Information Processing” will start in November 2021 (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 Expectation-Maximization (EM) algorithm and Variational Bayes (VB). 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
- As much as possible we use the Piazza course site for new announcements.
Instructors
- Prof.dr.ir. Bert de Vries (email: bert.de.vries@tue.nl) is the responsible instructor for this course and teaches all lectures with label B.
- Dr. Wouter Kouw (w.m.kouw@tue.nl) teaches all practical sessions on probabilistic programming with label W.
- Magnus Koudahl, MSc (m.t.koudahl@tue.nl) is the teaching assistant. Mr. Koudahl presents the “What is Life?" bonus lecture.
Materials
In principle, you can download all needed materials from the links below.
Books
Please download the following books/resources:
-
Under Construction: Bert de Vries et al. (2020), PDF bundle of lecture notes for lessons B0 through B12 (Ed. Q3-2019/20).
- The lecture notes may change a bit during the course, e.g., to process comments by students. A final PDF version will be posted after the last lecture.
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Christopher M. Bishop (2006), Pattern Recognition and Machine Learning. You can also buy a hardcopy, e.g. at bol.com.
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Ariel Caticha (2012), Entropic Inference and the Foundations of Physics.
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Under Construction: Wouter Kouw (2020), Julia and Jupyter Install Guide.
- Use this guide if you need help to install Julia and Jupyter, so that you can open and run the course notebooks on your own machine.
- You can test your installation by running the notebook called “Probabilistic-Programming-0.ipynb”, which can be downloaded from github (under
lessons/notebooks/probprog). Here is a video with step-by-step instructions on opening course notebooks.
Lecture notes, videos and exercises
You can access all lecture notes, videos and exercises online through the links below:
| Date | lesson | materials | |||
|---|---|---|---|---|---|
| video guides | lecture notes | live class (2020) | exercises | ||
| 17-Nov-2021 | B0: Course Syllabus B1: Machine Learning Overview |
B1 | B0, B1 | B0 | |
| 19-Nov-2021 | B2: Probability Theory Review | B2.1, B2.2 | B2 | B2 | B2-ex B2-sol |
| 24-Nov-2021 | B3: Bayesian Machine Learning | B3.1, B3.2 | B3 | B3 | B3-ex B3-sol |
| 26-Nov-2021 | W1: Probabilistic Programming 1 - Intro Bayesian ML | W1.1, W1.2, W1.3 | W1 | W1 | |
| 01-Dec-2021 | B4: Factor Graphs and the Sum-Product Algorithm | B4 | B4 | B4 | B4-ex B4-sol |
| 03-Dec-2021 | B5: Continuous Data and the Gaussian Distribution | B5.1, B5.2, B5.3 | B5 | B5 | B5-ex B5-sol |
| 08-Dec-2021 | B6: Discrete Data and the Multinomial Distribution | B6 | B6 | B6, review B1-B6 | B6-ex B6-sol |
| 10-Dec-2021 | W2: ProbProg 2 - MP & Analytical Bayesian Solutions | W2.1, W2.2, W2.3 | W2 | W2 | |
| 15-Dec-2021 | B7: Regression | B7 | B7 | B7 | B7-ex B7-sol |
| 17-Dec-2021 | B8: Generative Classification B9: Discriminative Classification |
B8, B9 | B8, B9 | B8-9 | B8-9-ex B8-9-sol |
| 22-Dec-2021 | W3: ProbProg 3 - Regression and Classification | W3.1, W3.2 | W3 | W3 | |
| 24-Dec-2021 | B10: Latent Variable Models and Variational Bayes | B10 | B10 | B10 | B10-ex B10-sol |
| break | |||||
| 12-Jan-2022 | B11: Dynamic Models | B11 | B11 | B11 | B11-ex B11-sol |
| 14-Jan-2022 | B12: Intelligent Agents and Active Inference | B12 | B12 | B12 | B12-ex B12-sol |
| 19-Jan-2022 | W4: ProbProg 4: Latent Variable and Dynamic Models | W4.1, W4.2, W4.3 | W4 | PP4 | |
| 21-Jan-2022 | M1: Bonus Lecture: What is Life? | M1 | M1 | ||
| 03-Feb-2022 | written examination (13:30-16:30) | ||||
| 22-Apr-2022 | resit written examination (18:00-21:00) | ||||
- Furthermore, Q&A for each lesson can be accessed at the Piazza course site.
- When you do the exercises, feel free to consult the following matrix and Gaussian cheat sheets (by Sam Roweis) when doing the exercises.
Study Guide
-
Please consult the Course Syllabus (lecture notes for 1st class) for advice on how to study the materials.
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Each year there will be two written exam opportunities. You cannot bring notes or books to the written exam sessions. All needed formulas are supplied at the exam sheet.
Exam Preparation
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
Grading
Under Construction: