Randomized Algorithms I
4
Algorithms and machine learning
Advanced studies
The course introduces a variety of tools from probability theory for designing and analysing randomized algorithms, and for analysing other probabilistic problems in computer science. Techniques include basic properties of discrete random variables, large deviation bounds, and balls and urns models. Applications include counting, distributed algorithms, and average case analysis. Prerequisites: Design and analysis of algorithms and a basic course in probabilities, or equivalent. Course book: M. Mitzenmacher, E. Upfal. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press 2005.
Exam
26.02.2013
16.00
A111
Year | Semester | Date | Period | Language | In charge |
---|---|---|---|---|---|
2013 | spring | 14.01-20.02. | 3-3 | English | Jyrki Kivinen |
Lectures
Time | Room | Lecturer | Date |
---|---|---|---|
Mon 10-12 | B119 | Jyrki Kivinen | 14.01.2013-20.02.2013 |
Wed 10-12 | B119 | Jyrki Kivinen | 14.01.2013-20.02.2013 |
Exercise groups
Time | Room | Instructor | Date | Observe |
---|---|---|---|---|
Thu 12-14 | C222 | Teppo Niinimäki | 21.01.2013—22.02.2013 |
General
Kurssin opetuskieli on englanti. Tarkemmat tiedot tulevat tämän sivun englanninkieliseen versioon.