Module Number INFO-4151 |
Module Title Applied Statistics II |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Workload:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Written Test |
|
Content | Building on "Applied Statistics I" (german: "Angewandte Statistik I"), more complex statistical methods are covered: Generalized Linear Models (GLM), Principal Component Analysis (PCA), Independence Analysis (ICA), and Bayesian statistics. The emphasis is on the practical application of all methods and their implementation in Python (with the modules statsmodels, scipy.stats, sklearn and pymc) and the presentation of results in notebooks. |
|
Objectives | The students know advanced statistical methods, how to use them and how to implement them in software. They can figure out the differences between frequentist and Bayesian statistics. |
|
Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
4
6.0
wt
90
g
100
|
|
Prerequisite for participation |
INF3223 Applied Statistics I, INFM1010 Mathematics for Computer Science 1: Analysis, INFM1020 Mathematics for Computer Science 2: Linear Algebra |
|
Lecturer / Other | Wannek | |
Literature | Wird in der Vorlesung bekannt gegeben |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |