Linear Algebra Methods for Data Mining
4
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").
Lectures
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
Time | Room | Instructor | Date | Observe |
---|---|---|---|---|
Fri 12-14 | BK106 | Saara Hyvönen | 19.01.2007—23.02.2007 |