Linear Algebra Methods for Data Mining

Algorithms and machine learning
Advanced studies
The course will cover linear algebra techniques useful in data exploration. Topics include matrix decompositions (SVD, QR) and related methods (principal component analysis, latent semantic indexing) and their application to data mining problems, e.g. information retrieval. Also eigenvalue problems related to ranking algorithms (Pagerank, HITS) are discussed. Both theoretical and implementational aspects are considered. Required background: basic linear algebra skills (e.g. course "Lineaarialgebra I").
Year Semester Date Period Language In charge
2008 spring 25.01-25.01. Finnish