PřF:Bi7490 Predictive Modelling - Course Information
Bi7490 Predictive Modelling
Faculty of ScienceSpring 2009
- Extent and Intensity
- 2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
- Teacher(s)
- prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Klára Komprdová, Ph.D. (seminar tutor) - Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D. - Prerequisites
- Bi5040 Biostatistics - basic course && Bi8600 Multivariate Statistical Meth.
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis. - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 11 fields of study the course is directly associated with, display
- Course objectives
- This course focuses on using of advanced parametric and non-parametric multivariate methods for spatial and predictive modelling (from basic regression continues to the latest non-parametric methods). Important subject is a comparison of advantages and disadvantages of individual methods on different data sets (from statistical and spatial distribution point of view). Each lecture block will be supplemeted with practical lesson on PC where different approaches will be tested on various SW. Real examples from experimental bilology, ecology and chemistry will be presented during these lectures.
- Syllabus
- Introduction to predictive modelling: Principles of multivariate statistics, Comparison of parametric and nonparametric methods, Demonstration various software (STATISTIKA, R-project, MATLAB)
- Parametric regression methods (LM, GLM, GAM): Assumptions, Limitations, and Practical Considerations (selection of link function, multicolinearity, estimate parameters, residuals, deviance etc.)
- Nonparametric methods I: Decision tree: Classification and regression tree (various algorithm of building tree, accuracy, stability, crossvalidation etc.)
- Nonparametric methods II: Bagging, Boosting, Arcing, Random forest
- Spatial analysis: Interpolation and Extrapolation, Spatial autocorrelation, Pseudoreplication, using parametrical and nonparametric methods for spatial modelling
- Real examples of predicting modelling: Predictive modelling of species occurrence, concentration of pollutants; selection indicative species
- Literature
- Lažanský et. Kol.: Umělá inteligence I.- IV.
- Jan Klaschka, Emil Kotrč: Klasifikační a regresní lesy, sborník konference ROBUST 2004
- Breiman, L. et al (1984) Classification and Regression Trees, Chapman and Hall
- Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data mining, Inference and Prediction, Springer 2003
- Hengl T. (2007) A Practical Guide to Geostatistical Mapping of Environmental Variables
- Lemeshow, Stanley & Hosmer, David W., Jr.. Logistic regression, p. 1-11. In Encyclopaedia of Biostatistics, 1st ed. [Online.] Wiley, London.
- Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
- McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
- Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
- McCullagh, P., Nelder, J.A. (1989): Generalized Linear Models (2nd edition), Chapman & Hall
- Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
- Harrel F. E., Jr. (2001): Regression Modeling Strategies. With Applications to Linear Models, Logistic Regression and Survival Analysis. Springer, Springer Series in Statistics, New York
- Assessment methods
- lectures and practice on PC; written tests
- Language of instruction
- Czech
- Further Comments
- The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week. - Teacher's information
- http://www.cba.muni.cz/vyuka/
- Enrolment Statistics (Spring 2009, recent)
- Permalink: https://is.muni.cz/course/sci/spring2009/Bi7490