Probabilistic Methods in Computer Science

Quick Facts

Lecturer:Jan Peters
Teaching Assistants:Yichen Cai, Anish Diwan, Claudius Kienle, Siwei Ju, Paul Jansonnie
Classes:Wed. 17:10 - 18:50, S311/08
Language:German, English
Office Hours:Tuesday 14:00 - 15:00 in S2|02 E202
Exam:TBD
TU-CAN:20-00-1150-iv Probabilistic methods in computer science
Credits:20-00-1150-iv Probabilistic methods in computer science: 5.0 CP
Moodle:https://moodle.informatik.tu-darmstadt.de/course/view.php?id=1874

Contents:

  • Basics from probability theory, statistics, and information theory
  • Probabilistic approaches to graph-based modelling in computer science
  • Basic probabilistic problems and use of probabilistic methods
- in practical computer science (e.g., run-time analysis of programs, data compression)
- in technical computer science (e.g., reliability of hardware, caching)
- in applied computer science (e.g., simulation of stochastic systems, probabilistic systems)
  • Selected randomized algorithms, their analysis by `The Probabilistic Method', algorithms for automated decision making and optimization
  • Application of probabilistic methods in artificial intelligence (e.g., learning methods, neural networks) and data science
  • Implementation of probabilistic methods by means of practical programming examples