263-5210-00L  Probabilistic Artificial Intelligence

SemesterAutumn Semester 2014
LecturersA. Krause
Periodicityyearly recurring course
Language of instructionEnglish



Catalogue data

AbstractThis course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet.
ObjectiveHow 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 upper-level undergraduate and graduate students.
ContentTopics covered:
- Search (BFS, DFS, A*), constraint satisfaction and optimization
- Tutorial in logic (propositional, first-order)
- Probability
- Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks)
- Probabilistic palnning (MDPs, POMPDPs)
- Reinforcement learning
- Combining logic and probability
Prerequisites / NoticeSolid basic knowledge in statistics, algorithms and programming

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersA. Krause
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
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Courses

NumberTitleHoursLecturers
263-5210-00 VProbabilistic Artificial Intelligence2 hrs
Fri10-12CHN C 14 »
A. Krause
263-5210-00 UProbabilistic Artificial Intelligence1 hrs
Fri13-14CHN C 14 »
14-15CHN C 14 »
A. Krause

Groups

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Restrictions

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Offered in

ProgrammeSectionType
Certificate of Advanced Studies in Computer ScienceFocus Courses and ElectivesWInformation
Computer Science MasterFocus Elective Courses Visual ComputingWInformation
Computer Science MasterFocus Elective Courses Information SystemsWInformation
Robotics, Systems and Control MasterArtificial IntelligenceWInformation