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447-6265-00L 2 Credits WBZ D-MATH
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Deep Learning: A Probabilistic Approach

Deep Learning: Ein probabilistischer Ansatz

Does not take place this semester.
VVZ CR n/a

Last Updated: 2026-02-05 16:29:40

Abstract

This course introduces probabilistic deep learning (DL). DL is used for data with complex features like images. We treat DL as probabilistic models, as a continuation of GLMs (logistic regression, ...). The models are fitted with maximum likelihood or Bayesian learning.

Objective

Der Kurs wird auf Deutsch gegeben. Alle Unterrichtsmaterialien sind auf Englisch, daher sind auch die Lernziele auf Englisch formuliert. You will learn about different neural network architectures (e.g. fully connected and convolutional neural networks) and how to choose the appropriate NN architecture for your task at hand. You will learn to model different outcome distributions such as Gaussians, Poissonians, or Multinomial for the task at hand. You will get practical experiences in setting up probabilistic DL models, learn how to tune them, and learn how to control the training procedure.

General Information

Language
German
Levels
WBZ
Frequency
Every two years

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Deep Learning: Ein probabilistischer Ansatz
Does not take place this semester. Blockkurs im FS 2025 03.02.25 10.02.25 17.02.25 24.02.25
No time listed 19.5 h semesterly

Offered In