A Graduate Course: |
Bayesian Networks (7.5 p)
This course is of interest for engineers, statisticians and computer scientists who work with,e.g., modelling of highly complex systems, signal processing, data mining, artificial intelligence, robotics, or need understanding of statistical models using probabilities factorized according to directed acyclic graphs (DAGs) and the algorithms for the updating of probabilistic uncertainty in response to evidence, and statistical learning of model parameters and structures.
Prerequisites: An undergraduate course in probability and statistics, an undergraduate course in discrete mathematics and algorithms,
Credit points : 7.5 p.
Examination : Homework assignments and computer exercises submitted to the examiner as a report .
FIRST LECTURE: Friday, april 10th of 2015 at 14.15 - 16.00 in room: seminarierummet 3733 (room 3733 7th floor), institutionen för matematik, KTH, Lindstedtsvägen 25. You need to wait for entry outside the gated door
LECTURES: Fridays at 14.15-16.00 Room: seminarierummet 3733 (room 3733 7th floor), institutionen för matematik, KTH, Lindstedtsvägen 25.
Homework assignments :
Topics for presentations :
Course schedule and information
Timo Koski Lecturer and Examiner
Department of Mathematics
Royal Institute of Technology
SE-100 44 Stockholm
Phone: +46-8-790 71 34
|Published by: Timo Koski