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Mathematical modelling of search engines

Code: 61514
ECTS: 5.0
Lecturers in charge: prof. dr. sc. Zlatko Drmač - Lectures
Lecturers: prof. dr. sc. Zlatko Drmač - Exercises

1. komponenta

Lecture typeTotal
Lectures 30
Exercises 15
* Load is given in academic hour (1 academic hour = 45 minutes)
COURSE AIMS AND OBJECTIVES: To specify and implement some algorithms for intelligent data search and data analysis.

1. [1] Motivation. Examples of intelligent data analysis. Search engines.
2. [5] Vector model of data. Noise reduction. Best lover rank approximation. SVD and latent semantic indexing. Methods and applications (Internet, protein structure).
3. [4] Grouping algorithms. Spectral partition of weighted graphs. Laplace matrix and Fiedler vector. Probabilistic approaches.
4. [4] Page ranking. Hubs and authorities. Dead ends and spider traps. Markov models. Practical methods.
  1. R. B. Yates, B. R. Neto: Modern Information Retrieval
  2. S. Mitra, T. Acharya: Data Mining
  3. M. W. Berry, Z. Drmač, E. R. Jessup: Using linear algebra for information retrieval
  4. C. H. Q. Ding, H. Zha, X. He, P. Husbands, H. D. Simon: Link analysis: Hubs and Authorities on the World Wide Web
  5. M. Bianchini, M. Gori, F, Scarselli: Inside PageRank
Prerequisit for:
Enrollment :
Passed : Introduction to data mining
3. semester
Izborni predmeti 5, 6, 7 - Mandatory studij - Mathematical Statistics

4. semester
Izborni predmeti 5, 6, 7 - Mandatory studij - Mathematical Statistics
Consultations schedule:


Link to the course web page: http://www.pmf.unizg.hr/math/predmet/mmp_a