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?
|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
|12 May:||Algorithms for point estimation
Readings: EE94, G99
|19 May:||Algorithms for regression
|26 May:||Algorithms for clustering Readings: KMNPSW99, E97|
|2 Jun:||Algorithms for hierarchical clustering Readings: E98|
Dept. Information & Computer Science,