Probabilistic Programming (Proseminar)
The world is full of randomness and uncertainty. Modeling random processes helps us make better decisions, but it is not always intuitive how to work with these models. Probabilistic programming languages enable us to describe complicated probability models and obtain properties relevant for decision making through the power of modern computers. In this module, we learn the basics of probabilistic programming using the language WebPPL.
Organization
We work with the book Probabilistic Models of Cognition. We meet every week. Each meeting is about a chapter of the book. One of you will teach the chapter to us others and explain how to solve the chapter’s exercises during the meeting. Active participation in the discussion is expected. Until the end of the semester you will hand in a report on the topic you presented, based on the book chapter but also on additional material. Your grade will be based on the presentation of the book chapter and the report you handed in, weighted equally. Depending on the participants the course will be in English or German.
- Link to Alma: Alma
Meetings
Our meetings are on Wednesday at 5pm s.t. at Sand in C215.
No meeting on May 24 and on May 31.
The normal schedule resumes on June 7.
Handin
Write a short tutorial on the topic of the chapter that you have presented. Come
up with at least one original example illustrating the point. Please write at
most 4 pages. Send the document until September 29 as a single PDF file via
email to Philipp Schuster
ResearcherPhilipp Schuster.
Use the following template:
https://www.acm.org/publications/proceedings-template
Enrolling
To participate in this course, send an email containing your name, Matrikelnummer and Studiengang to Philipp Schuster.
Reading
If you want to get an impression of what probabilistic programming is you can check out Making Money Using Math by Erik Meijer.
People
- Jonathan Brachthäuser
Head of the SE research groupJonathan Immanuel Brachthäuser - Philipp Schuster
ResearcherPhilipp Schuster
Prerequisites
Knowledge in probability theory is helpful but not required.