Matrix and tensor methods in data analysis

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Matrix and tensor methods in data analysis

Code: 239832
ECTS: 5.0
Lecturers in charge: prof. dr. sc. Zlatko Drmač
Take exam: Studomat
Load:

1. komponenta

Lecture typeTotal
Lectures 45
* Load is given in academic hour (1 academic hour = 45 minutes)
Description:
Literature:
  1. The 25,000,000,000 Eigenvector: The Linear Algebra behind Google., Kurt Bryan, Tanya Leise, SIAM Review Vol. 48, No. 3, 2006.
  2. Link Analysis: Hubs and Authorities on the World Wide Web, Chris H. Q. Ding, Hongyuan Zha, Xiaofeng He, Horst D. Simon, SIAM Review 46(2), 2002.
  3. A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching, V. D. Blondel, A.í Gajardo, M. Heymans, P. Senellart, P. Van Dooren, SIAM Review 46(4), 2004.
  4. Spectral relaxation for k-means clustering, H. Zha, X. He, CH. Ding. H. Simon, M. Gu, NIPS, 2001.
  5. Tensor Decompositions and Applications, T. G. Kolda, B. W. Bader, SIAM Review 51(3), 2009.
  6. Higher-order web link analysis using multilinear algebra, T.Kolda, B. Bader, J. Kenny, Sandia Tech Report, 2005.
1. semester
Izborni predmet 1 i 2 - Regular study - Financial and Business Mathematics

3. semester
Izborni predmet 4, 5, 6 - Regular study - Financial and Business Mathematics

4. semester Not active
Izborni predmet 4, 5, 6 - Regular study - Financial and Business Mathematics
Consultations schedule: