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.
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