Prof. Dr. Luca Amendola
Tel: +49-6221-549-407 -- Fax: +49-6221-549-333 Sprechstunde:
After my lectures |
Computational Statistics and Data Analysis
Summer Semester 2025
The course is an introduction to statistical analysis, with emphasis on computational methods. We will use Python for some exercises, but students can use their preferred programming language (e.g. Python, Mathematica, R, etc), or even pseudo-code.Program
- Random variables
- Probability distribution functions
- Moments, generating function, central limit theorem etc
- Bayes theorem
- Linear and non-linear regression
- Estimators (maximum likelihood, robust estimators etc)
- Hypothesis testing (non-parametric, multiple, etc)
- Cluster analysis
- Principal Component Analysis
- Bayesian model selection
- Fisher matrix
- Random fields
- Basics of Machine Learning
NOTE: Course begins on April 16th, 2025
Classes on Wednesday 11:15-13:00 in INF 227
Exercise classes start on the week of April, 28th
18/4: publication of the first exercise sheet to be discussed during the week of 28/4
Week of 21/4: no tutorials
Week of 28/4: first student discussion on the exercise sheet published on 18/4
CREDITS: 6
During the course homework sheets will be handed out (you will be able to download them from this page).
Roles for the admission to the exam = what you need to check during the tutorials:
Attend at least 70% of the tutorials. If attendance is <70%, it is required to hand in 3 full exercise sheets, which will be graded. AND (not OR) Earn 3 points by: Presenting two halves of different exercise sheets (1 point) Actively participating in the discussion during tutorials (max 0.5 points per tutorial).
Suggested texts:
Lecture notes
Slides on Machine Learning I
Slides on Machine Learning II
DeGroot and Schervish, Probability and Statistics, Addison Wesley
P. Gregory, Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press
Introduction to R
(an introductory manual on R language)
