107.386 Classification and Discriminant Analysis
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2019W, VU, 3.0h, 4.5EC
TUWEL

Properties

  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to

  • explain and formulate the theoretical concepts of important dimension reduction techniques and methods for linear and nonlinear regression and classification
  • identify the strengths and weaknesses of the different statistical methods and tools and to use them in practice

Subject of course

During the past decade there has been an explosion in computation and information technology. With it has come vast amount of data in a variety of fields such as medicine, finance and marketing. The challange to understand these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning and bioinformatics. This lecture describes the important ideas in these areas in a common conceptual framework. The many topics include neural networks, support vector machines, classification trees and generalized additive models.

Teaching methods

Examples with data, software environment R

Mode of examination

Written and oral

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Wed09:00 - 11:0002.10.2019 - 29.01.2020EI 5 Hochenegg HS Vorlesung Filzmoser
Thu12:00 - 13:0003.10.2019 - 30.01.2020FH Hörsaal 6 - TPH Vorlesung Filzmoser
Classification and Discriminant Analysis - Single appointments
DayDateTimeLocationDescription
Wed02.10.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu03.10.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed09.10.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu10.10.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed16.10.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu17.10.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed23.10.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu24.10.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed30.10.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Wed06.11.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu07.11.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed13.11.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu14.11.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed20.11.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu21.11.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed27.11.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu28.11.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed04.12.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser
Thu05.12.201912:00 - 13:00FH Hörsaal 6 - TPH Vorlesung Filzmoser
Wed11.12.201909:00 - 11:00EI 5 Hochenegg HS Vorlesung Filzmoser

Examination modalities

Solving examples in R, oral exam

Course registration

Not necessary

Curricula

Study CodeObligationSemesterPrecon.Info
066 926 Business Informatics Mandatory elective
066 936 Medical Informatics Mandatory elective
860 GW Optional Courses - Technical Mathematics Not specified

Literature

Lecture notes for this course are available from the lecturer.

Miscellaneous

Language

German