This 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.
Objective
How 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.
Content
Topics 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 / Notice
Solid basic knowledge in statistics, algorithms and programming
Performance assessment
Performance assessment information (valid until the course unit is held again)
The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination
written 120 minutes
Additional information on mode of examination
Die Prüfung kann am Computer stattfinden / The exam might take place at a computer.
Written aids
Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. A simple calculator will be provided on the computers where you will be taking the exam.
Online examination
The 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.
Performance assessment as a semester course