Oct 14, 2019   6:51 p.m. Boris
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Course syllabus N400C2_4D - Chemometrics II (FCFT - 2019/2020 - post-graduate studies)


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University: Slovak University of Technology in Bratislava
Faculty: Faculty of Chemical and Food Technology
Course unit code: N400C2_4D
Course unit title: Chemometrics II
Mode of delivery, planned learning activities and teaching methods:
lecture2 hours weekly (on-site method)

 
Credits allocated: 3
 
Recommended semester/trimester: Analytical Chemistry - doctoral (optional), 1. year
Level of study: 3.
Prerequisites for registration: none
 
Assesment methods:
Elaborating a separate final thesis (maximum 40 points) and its presentation (maximum 10 points). Oral and written exam (maximum 50 points).

Final evaluation of success will be carried out according to the grading scale approved by the faculty.
 
Learning outcomes of the course unit:
The student will acquire theoretical and practical knowledge of how to process, evaluate and present data from analytical measurements with targeted use of mathematics, statistics and chemometrics to obtain information in various application areas of qualitative and quantitative analysis.
 
Course contents:
Types of variables in the multivariate analysis
Multivariate normal distribution
Multivariate linear regression models
Principal components methods
Factor analysis
Canonical correlation analysis
Discrimination and classification
Neural networks and genetic algorithm
Importance variable and dimension reduction of variables
Data mining
Visualization of multidimensional data
 
Recommended or required reading:
Basic:
MILLER, J N. -- MILLER, J C. Statistics and Chemometrics for Analytical Chemistry. Harlow: Pearson Education Limited, 2010. 296 p. ISBN 978-0-273-73042-2.
OTTO, M. Chemometrics: Statistics and Computer Application in Analytical Chemistry. Weinheim: Wiley-VCH, 2007. 343 p. ISBN 978-3-527-31418-8.
MELOUN, M. -- MILITKÝ, J. Kompendium statistického zpracování dat. Praha: Academie, 2006. 970 p. ISBN 80-200-1396-2.
MELOUN, M. -- MILITKÝ, J. Interaktivní statistická analýza dat. Praha: Karolinum, 2012. 960 p. ISBN 978-80-246-2173-9.

Recommended:
JOHNSON, R A. -- WICHERN, D W. Applied Multivariate Statistical Analysis. Upper Saddle River, NY: Pearson Education, Inc., 2008. 800 p. ISBN 978-0-1318-7715-3.
LAROSE, D T. DATA MINING METHODS AND MODELS. Hoboken, New Jersey: John Wiley & Sons, Inc., 2006. 344 p. ISBN 978-0-471-66656-1.

 
Language of instruction: slovak and english
 
Notes:
 
Courses evaluation:
Assessed students in total: 10

PN
100,0 %0 %
Name of lecturer(s): prof. Ing. Ján Labuda, DrSc. (person responsible for course) - slovak, english
 
Last modification: 26. 4. 2019
Supervisor: prof. Ing. Ján Labuda, DrSc. and programme supervisor


Last modification made by Ing. Tomáš Molnár on 04/26/2019.

Type of output: