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
2007 spring 16.01-21.02. English


Time Room Lecturer Date
Tue 12-14 C220 Saara Hyvönen 16.01.2007-21.02.2007
Wed 14-16 C220 Saara Hyvönen 16.01.2007-21.02.2007

Exercise groups

Group: 1
Time Room Instructor Date Observe
Fri 12-14 BK106 Saara Hyvönen 19.01.2007—23.02.2007