In the first part of the course, we'll go over various estimation methods, with an emphasis on robustness: what sort of errors can a given method tolerate and still provably return an accurate estimate of the data?
In the second part, we'll read and discuss about algorithmics: what techniques (primarily from computational geometry) can or have been used to implement or approximate these estimators efficiently?
| 7 Apr: | Introduction |
| 14 Apr: | Methods for point estimation Reading: ABET98 through section 2.5 (description and proof of existence of centerpoints) |
| 21 Apr: | Methods for regression Readings: HR98, RH99, ABET98 |
| 28 Apr: | Methods for clustering Readings: BE96, KMNPSW99 |
| 5 May: | Methods for hierarchical clustering Readings: RW97 |
| 12 May: | Algorithms for point estimation Readings: EE94, G99 |
| 19 May: | Algorithms for regression Readings: DMN92 |
| 26 May: | Algorithms for clustering Readings: KMNPSW99, E97 |
| 2 Jun: | Algorithms for hierarchical clustering Readings: E98 |
| 9 Jun: | Review |
David Eppstein,
Theory Group,
Dept. Information & Computer Science,
UC Irvine.
Last update: