[ 536DASCSTAU19 ] UE 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 Students will become familiar with the methods presented in the corresponding lecture and can use R for solving statistical problems
  • 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 distribution
  • Estimating parameters: point estimation and confidence interval estimation
  • Statistical hypothesis testing
  • Introduction into R
Criteria for evaluation Homework assignments and presentations
Methods Student presentations
Language English
Study material
  • Slides provided by instructor
  • Bortz, J.: Statistik für Human- und Sozialwissenschaftler. Springer, Heidelberg, in der aktuellen Auflage.
  • Fahrmeir, L.; Künstler, R.; Pigeot, I.: Statistik – Der Weg zur Datenanalyse. Springer, Berlin, in der aktuellen Auflage.
  • Hartung, J.; Elpelt, B.; Klösener, K.-H.: Statistik: Lehr- und Handbuch der angewandten Statistik. Oldenbourg, München, in der aktuellen Auflage.
  • 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 35
Assignment procedure Direct assignment