Matrix and tensor methods in data analysis

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Matrix and tensor methods in data analysis

Code: 239823
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. Kurt Bryan, Tanya Leise: The 25,000,000,000 Eigenvector: The Linear Algebra behind Google.
  2. Chris H. Q. Ding, Hongyuan Zha, Xiaofeng He, Horst D. Simon: Link Analysis: Hubs and Authorities on the World Wide Web
  3. V. D. Blondel, A.í Gajardo, M. Heymans, P. Senellart, P. Van Dooren: A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching
  4. H. Zha, X. He, CH. Ding. H. Simon, M. Gu: Spectral relaxation for k-means clustering
  5. T. G. Kolda, B. W. Bader: Tensor Decompositions and Applications
  6. T.Kolda, B. Bader, J. Kenny: Higher-order web link analysis using multilinear algebra
1. semester Not active
Izborni predmet 1, 2, 3, 4 - Regular study - Mathematical Statistics

2. semester
Izborni predmet 1, 2, 3, 4 - Regular study - Mathematical Statistics

3. semester Not active
Izborni predmeti 5, 6, 7 - Regular study - Mathematical Statistics

4. semester
Izborni predmeti 5, 6, 7 - Regular study - Mathematical Statistics
Consultations schedule: