263-5210-00L  Probabilistic Artificial Intelligence

SemesterAutumn Semester 2021
LecturersA. Krause
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
263-5210-00 VProbabilistic Artificial Intelligence
Fr 10-12 und 13-14 im ETA F5 mit Videoübertragung ins ETF E1
3 hrs
Fri10-12ETA F 5 »
10-12ETF E 1 »
13-14ETA F 5 »
13-14ETF E 1 »
A. Krause
263-5210-00 UProbabilistic Artificial Intelligence
Q&A session: Monday, 17-18, via zoom
2 hrs
Thu16-18CHN C 14 »
A. Krause
263-5210-00 AProbabilistic Artificial Intelligence2 hrsA. Krause

Catalogue data

AbstractThis 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.
ObjectiveHow 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.
ContentTopics 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
Prerequisites / NoticeSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersA. Krause
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examination70% 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.
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator.
Online examinationThe examination may take place on the computer.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
Moodle courseMoodle-Kurs / Moodle course
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places700 at the most
Waiting listuntil 04.10.2021

Offered in

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Data Science MasterCore ElectivesWInformation
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Electrical Engineering and Information Technology MasterSpecialisation CoursesWInformation
Computer Science MasterCore CoursesWInformation
Computer Science MasterFocus Elective Courses Visual ComputingWInformation
Computer Science MasterFocus Elective Courses General StudiesWInformation
Computer Science MasterMinor in Machine LearningWInformation
Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Mathematics MasterMachine LearningWInformation
Computational Science and Engineering BachelorRoboticsWInformation
Computational Science and Engineering MasterRoboticsWInformation
Robotics, Systems and Control MasterCore CoursesWInformation
Statistics MasterSubject Specific ElectivesWInformation