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263-5210-00L 8 Credits BSC , MSC , WBZ D-BSSE , D-INFK , D-MATH , D-ITET , D-MAVT
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Probabilistic Artificial Intelligence

Lecturers & Examiners: Prof. Dr. Andreas Krause
VVZ CR 4.01

Last Updated: 2026-02-05 16:01:55

Abstract

This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.

Objective

How can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students.

Content

Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning

Resources

Learning Materials (Links)

General Information

Language
English
Levels
BSC , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator.
Digital
The exam takes place on devices provided by ETH Zurich.
70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function.The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory.Failing the project results in a failing grade for the overall examination of Probabilistic Artificial Intelligence (263-5210-00L).Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show.

Registration & Places

Max Places
900

Course Components

Type Title Time & Place Hours
lecture Probabilistic Artificial Intelligence
Fr 10-12 und 13-14 im ETA F5 mit Videoübertragung ins ETF E1
  • Fri 10:15-12:00 (ETA F 5)
  • Fri 10:15-12:00 (ETF E 1)
  • Fri 13:15-14:00 (ETA F 5)
  • Fri 13:15-14:00 (ETF E 1)
3 h weekly
exercise Probabilistic Artificial Intelligence
Q&A session: Monday, 17-18, via zoom
  • Thu 16:15-18:00 (CHN C 14)
  • Thu 16:15-18:00 (HG F 7)
2 h weekly
independent project Probabilistic Artificial Intelligence No time listed 2 h weekly

Offered In