Sommersemester 24

Vorlesung

Statistical Learning

Dozent:
  • Prof. Dr. Thomas Deckers
Ansprechpartner:
Semester:
Sommersemester 2024
Turnus:
Blockveranstaltung
Raum:
hybrid
Beginn:
23.05.2024
Ende:
06.06.2024
Sprache:
deutsch/englisch
Moodle:
Veranstaltung in Moodle
LSF:
Veranstaltung im LSF
Verknüpfte Veranstaltungen:

Wichtige Hinweise:

The Moodle password is: StatLearn24

For additional information on the exercise, please see the corresponding entry.

Beschreibung:

Statistical learning is a field that teaches students how to analyze and interpret data by applying statistical methods and machine learning algorithms to uncover patterns, make predictions, and gain insights from data.

The course is designed for master's or PhD. students. Due to the significant programming component, prior knowledge in R or other programming languages is desirable but not required.

Gliederung:

The syllabus includes: statistical and machine learning methods, in particular: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

Prüfungsart:

Report and Presentation.

Formalia:

02.05 Preliminary meeting, via Zoom.
23.05 In-person lecture, room tba.
24.05 Lecture, hybrid.
06.06 Lecture, hybrid.

The zoom-link will be posted soon.

In-person meetings can be accessed via live-stream.

There are a total of 5-6 tutorials scheduled, each on Fridays from 8.30-12 a.m., starting on 07.06.

General Information on the module can be found in the course catalogue