263-5210-00L  Probabilistic Artificial Intelligence

SemesterHerbstsemester 2020
DozierendeA. Krause
Periodizitätjährlich wiederkehrende Veranstaltung


263-5210-00 VProbabilistic Artificial Intelligence
The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality.
3 Std.
Fr10:15-12:00ETA F 5 »
13:15-14:00ETA F 5 »
A. Krause
263-5210-00 UProbabilistic Artificial Intelligence2 Std.
Do16:00-18:00ON LI NE »
A. Krause
263-5210-00 AProbabilistic Artificial Intelligence2 Std.A. Krause


KurzbeschreibungThis 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 and the Internet.
LernzielHow can we build systems that perform well in uncertain environments and unforeseen situations? 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 sensor networks, robotics, and the Internet. The course is designed for graduate students.
InhaltTopics 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
Voraussetzungen / BesonderesSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte8 KP
PrüfendeA. Krause
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusschriftlich 120 Minuten
Zusatzinformation zum Prüfungsmodus70% 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.

Die Prüfung kann am Computer stattfinden / The exam might take place at a computer.

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.
Hilfsmittel schriftlichTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator.
Online-PrüfungDie Prüfung kann am Computer stattfinden.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.


Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


PlätzeMaximal 700
WartelisteBis 28.09.2020

Angeboten in

CAS in InformatikFokusfächer und WahlfächerWInformation
Computational Biology and Bioinformatics MasterTheorieWInformation
Cyber Security MasterWahlfächerWInformation
Cyber Security MasterWahlfächerWInformation
DAS in Data ScienceMachine Learning and Artificial IntelligenceWInformation
Data Science MasterWählbare KernfächerWInformation
Elektrotechnik und Informationstechnologie MasterEmpfohlene FächerWInformation
Elektrotechnik und Informationstechnologie MasterVertiefungsfächerWInformation
Informatik MasterKernfächerWInformation
Informatik MasterWahlfächer der Vertiefung General StudiesWInformation
Informatik MasterWahlfächer der Vertiefung in Visual ComputingWInformation
Informatik MasterWahlfächer der Vertiefung in Information SystemsWInformation
Informatik MasterErgänzung in Machine LearningWInformation
Maschineningenieurwissenschaften MasterRobotics, Systems and ControlWInformation
Rechnergestützte Wissenschaften BachelorRobotikWInformation
Rechnergestützte Wissenschaften MasterRobotikWInformation
Robotics, Systems and Control MasterKernfächerWInformation
Statistik MasterFachbezogene WahlfächerWInformation