Teaching

Information for Students

• The office hours of Prof. Klein are by appointment.
• For details regarding available Master's Theses, please see the corresponding section below.

• Winter Semester 2025/2026

  Advanced Bayesian Data Analysis (5 ECTS)

  This module contains the following course components:

  Lecture: Thursdays, 09:45 - 11:15 (SCC-CS 20.20, R167) Tutorial (every second week): Thursdays, 11:30 - 13:00 (SCC-CS 20.20, R167)

  ILIAS

  Praxis der Forschung (24 ECTS; two semesters)

  This semester, we offer the following project: "Applying Causal Machine Learning in Conflict Research" (supervised by Prof. Dr. Nadja Klein, Agostino Ruta and Jan Wenkel).

  For more details, see the overview page of this course.

  Show previous semesters

  Summer Semester 2025

  Research Seminar in Selected Topics in Statistical Learning and Data Science (3 ECTS)

  Thursdays, 11:30 - 13:00 (CS 20.20, Room 267)

  ILIAS

  The seminar will focus on methods for Bayesian computation. More details follow in the first session (24.04.2025).

  Praxis der Forschung (24 ECTS; two semesters)

  This semester, we offer the following project: "Probabilistic Object Detection with Conformal Prediction" (supervised by Prof. Dr. Nadja Klein, Moussa Kassem Sbeyti and Dr. Nicolas Bianco)

  For more details, see the overview page of this course.

  Winter Semester 2024/2025

  Advanced Bayesian Data Analysis (5 ECTS)

  Every second week, the following course components take place

  Lecture: Tuesdays, 11:30 - 13:00 (50.19, Seminarraum 1), Wednesdays 09:45 - 11:15 (50.28, Seminarraum 2) Tutorial: Tuesdays, 09:45 - 11:15 (50.19, Seminarraum 5)

  ILIAS

  More details follow in the first session (22.10.2024), where no Tutorial takes place.

  Praxis der Forschung (24 ECTS; two semesters)

  This semester, we offer the following two projects:

  1. Efficient NUTS Sampler for Bayesian Conditional Transformation Models (supervised by Prof. Dr. Nadja Klein)
  2. Self-Supervised Learning for Real-World Object Detection (supervised by Prof. Dr. Nadja Klein and Moussa Kassem Sbeyti)

  For more details, see the overview page of this course.

• Master Theses

  We offer Master theses at the intersection of statistics and machine learning. Please see below for titles of available theses in these areas. If you are interested in one of these projects and fulfill the requirements laid out in our thesis guidelines, please contact us for more details.

  • Active Learning via Uncertainty Estimation for Deep Learning Tasks with Costly Data Acquisition
  • Runtime Verification of Computer Vision Deep Neural Networks against Symbolic Constraints (in collaboration with the University of Lübeck)
  • Understanding And Implementing Predictive Information Criteria For Bayesian Models
  • Asymptotic Behaviour Of The Posterior In Overfitted (Deep) Mixture Models
  • Posterior Concentration Rates For Bayesian High-Dimensional Sparse Additive Models
  • Uncertainty Quantification For Deep Learning
  • Using Stacking To Average Distributional Regression Models
  • Measuring The Explained Variance In Structured Additive Distributional Regression
  • Comparisons And Implementation Of Non-Local Shrinkage Priors
  • Approximate Bayesian Multivariate Spatial Factor Analysis
  • Targeted Sampling Based On Epistemic Uncertainty For Improving Predictive Performance Of Dl Tasks In The Case Of Costly Data Acquisition
  • Uncertainty Quantification For Complex-Valued Deep Neural Networks
  • Variational Inference for Distributional Regression
  • Informed Shrinkage In Bayesian Graphical Modeling Via External Networks
  • Basis Selection In Bayesian Semi-Parametric Regression Using Increasing Shrinkage Priors
  • Analysis of routine data from outpatient medical care (in collaboration with the University of Lübeck)

  
  

  A selection of completed theses at the chair can be found below.

  Show completed theses

  Master Theses

  • Analyzing Heat and Experienced Racial Segregation using Large-Scale Foot Traffic Data
  • A Python Implementation for the Structural Topic Model
  • Optimierung der Kraftwerkssteuerung mittel Reinforcement Learning unter Einsatz von kurzfristigen Ausgleichsenergiepreiseprognosen
  • Including Deep Neural Network Architectures into Multistage Intensity Models: An Application to Credit Risks
  • MultiFlags and LatentFlags: A Probabilistic Framework for Size Advice in Fashion E-Commerce
  • Interpretable Modelling of ICU Patients Remaining Length-of-Stay Distribution using Tabular Patient Data, Clinical Notes and Irregularly Spaced Clinical Measurements
  • Using Variational Inference to Estimate Structred Additive Distributional Regression Models
  • Elastic Full Procluster Means for Sparse and Irregular Curves
  • Reconstructing Multivariate Functional Data with Medical Applications
  • Investment Constraints in Southern Europe: A Spatial Econometric Analysis of World Bank Enterprise Surveys

  Bachelor Theses

  • Application of Regression Trees on Compositional Data Using European Parliament Election Results
  • Die Modellierung der COVID-19 Fallzahlen in Abhängigkeit von Strukturdaten zu Wetter und Bevölkerung in Berlin
  • Vergleich von Vorhersagemodellen zu Stornierungen von Hotelbuchungen