Bi7490 Advanced non-parametric methods

Faculty of Science
Spring 2011 - only for the accreditation
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
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
Course objectives
At the end of the course, students should be able to:
- critically evaluate the data set in terms of distribution of data
- determine the space structure of the data
- use basic methods for spatial and predictive modeling
- acquisition of various software to create models(R-project, Matlab, Statistica)
- select an appropriate predictive method based on the distribution of data
- compare the advantages and disadvantages of different methods
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
  • Breiman L. (2001) Random forests. Machine Learning 45, pp. 5 32.
  • 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
  • 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
  • Breiman L. (1996) Bagging predictors. Machine Learning 24, pp.123 140.
  • McCullagh, P., Nelder, J.A. (1989): Generalized Linear Models (2nd edition), Chapman & Hall
  • 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
  • Lemeshow, Stanley & Hosmer, David W., Jr.. Logistic regression, p. 1-11. In Encyclopaedia of Biostatistics, 1st ed. [Online.] Wiley, London.
  • Legendre P., Legendre L. (1998) Numerical ecology (second ed.), Elsevier, Amsterdam
  • McCullagh C. E., Searle S. R. (2001): Generalized, Linear, and Mixed Models, John Wiley & Sons.
Teaching methods
Education is performed as lectures with PowerPoint presentation. 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. Students are asked to interpret results of practical examples. Student develop a project on a selected topic during the semester.
Assessment methods
Final assesment (at the end of semester) is combination of written examination and project evaluation.
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/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Autumn 2014, Autumn 2015, Autumn 2019, Autumn 2020, autumn 2021.