[ 536DASCSTAV19 ] VL Statistics for AI

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B1 - Bachelor's programme 1. year (*)Artificial Intelligence Thomas Forstner 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2019W
Objectives Introduction into the research methodology in statistics and the basic principles of statistical thinking
  • Descriptive statistics: one and two dimensional characteristics
  • Correlation and linear regression
  • Basic concept of probability theorie including Bayes' theorem
  • Theory of random variables
  • Special distributions, test distributions
  • Estimating parameters: point estimation and interval estimation
  • Statistical hypothesis testing
Criteria for evaluation Examination
Methods Lecture by instructor
Language English
Study material
  • Slides provided by instructor
  • Bortz, J.: Statistik für Human- und Sozialwissenschaftler. Springer, Heidelberg
  • Fahrmeir, L.; Künstler, R.; Pigeot, I.: Statistik – Der Weg zur Datenanalyse. Springer, Berlin
  • Hartung, J.; Elpelt, B.; Klösener, K.-H.: Statistik: Lehr- und Handbuch der angewandten Statistik. Oldenbourg
  • or any other introductory book to statistics
Changing subject? No
Further information Continuative courses:

  • Statistics 2 (focus on parametric and non-parametric tests)
  • Special Topics: Computer Assisted Statistics with SPSS (introduction into SPSS)
  • Special Topics: Biostatistics in Clinical Research (introduction into biostatisics)
  • Special Topics: Applied Biostatistics
  • Special Topics: Statistics 3 (parametric and non-parametric analysis of variance, multiple linear and non-linear regression )
On-site course
Maximum number of participants -
Assignment procedure Direct assignment