SUBJECT
Title
Data mining in physics
Type of instruction
lecture
Level
master
Faculty
Part of degree program
Credits
4
Recommended in
Semester 3
Typically offered in
Autumn semester
Course description
During the course the students get acquainted with modern methods of analyzing large data sets and learn to perform such analysis. This is the introductory of a series of lectures, where time series analysis, principal component analysis, exploration of correlations, noise, artificial intelligence, databases, signal processing, cluster detection, classification and optimization are studied.
Readings
recommended readings:
- Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.
- Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.
- Edelstein, H., A. (1999). Introduction to data mining and knowledge discovery (3rd ed). Potomac, MD: Two Crows Corp.
- Han, J., Kamber, M. (2000). Data mining: Concepts and Techniques. New York: Morgan-Kaufman.
- Westphal, C., Blaxton, T. (1998). Data mining solutions. New York: Wiley.
- www.crisp-dm.org
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
- Pregibon, D. (1997). Data Mining. Statistical Computing and Graphics, 7, 8.
- Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan-Kaufmann.
- Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery & data mining. Cambridge, MA: MIT Press.
- Weiss, S. M., & Indurkhya, N. (1997). Predictive data mining: A practical guide. New York: Morgan-Kaufman.