Theses
The chair typically offers various thesis topics each semester in the areas computational statistics, machine learning, data mining, optimization and statistical software. You are welcome to suggest your own topic as well.
Before you apply for a thesis topic make sure that you fit the following profile:
- Knowledge in machine learning.
- Good R or python skills.
Before you start writing your thesis you must look for a supervisor within the group.
Send an email to the contact person listed in the potential theses topics files with the following information:
- Planned starting date of your thesis.
- Thesis topic (of the list of thesis topics or your own suggestion).
- Previously attended classes on machine learning and programming with R.
Your application will only be processed if it contains all required information.
Potential Thesis Topics
[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]
Below is a list of potential thesis topics. Before you start writing your thesis you must look for a supervisor within the group.
Available thesis topics
| Title | Type | Supervisor |
|---|---|---|
| Automated Error Detection in Radiology Reports with LLMs | MA | Aßenmacher |
| Open Topic in Interpretable Machine Learning | MA | Casalicchio |
| Benchmark Ordinal Regression Approaches in ML | MA | Casalicchio |
| Tailoring Interpretation Methods for Ordinal Regression | MA | Casalicchio |
| Predicting User Smartphone Behavior using Hawkes Processes | MA | Rügamer |
| Nutrition and time-to-event outcomes in critically ill patients: Multi-state modeling with cumulative effects | MA | Bender |
| Wildlife Image Classification - Detection of Outliers | MA | Bothmann |
| Wildlife Image Classification - Detection of New Classes | MA | Bothmann |
| Multi-Fidelity Bayesian optimization with an improved model across fidelities | MA | Feurer |
| Advanced Surrogate Models for Benchmarking | MA | Feurer |
| AutoML for X | MA | Feurer |
| Multi-Objective Ensembling for AutoML Systems | MA | Feurer |
| Tuning Strategies for Boosting AlgorithmsPage | MA | Feurer |
| Multi-Objective Gradient Descent | MA | Feurer |
| AutoML for fairness by abstaining | MA | Feurer |
Disputation
Procedure
The disputation of a thesis lasts about 60-90 minutes and consists of two parts. Only the first part is relevant for the grade and takes 30 minutes (bachelor thesis) and 40 minutes (master thesis). Here, the student is expected to summarize his/her main results of the thesis in a presentation. The supervisor(s) will ask questions regarding the content of the thesis in between. In the second part (after the presentation), the supervisors will give detailed feedback and discuss the thesis with the student. This will take about 30 minutes.
FAQ
- How do I prepare for the disputation?
You have to prepare a presentation and if there is a bigger time gap between handing in your thesis and the disputation you might want to reread your thesis.
- How many slides should I prepare?
That’s up to you, but you have to respect the time limit. Prepariong more than 20 slides for a Bachelor’s presentation and more than 30 slides for a Master’s is VERY likely a very bad idea.
- Where do I present?
Bernd’s office, in front of the big TV. At least one PhD will be present, maybe more. If you want to present in front of a larger audience in the seminar room or the old library, please book the room yourself and inform us.
- English or German?
We do not care, you can choose.
- What do I have to bring with me?
A document (Prüfungsprotokoll) which you get from “Prüfungsamt” (Frau Maxa or Frau Höfner) for the disputation.Your laptop or a USB stick with the presentation. You can also email Bernd a PDF.
- How does the grading work?
The student will be graded regarding the quality of the thesis, the presentation and the oral discussion of the work. The grade is mainly determined by the written thesis itself, but the grade can improve or drop depending on the presentation and your answers to defense questions.
- What should the presentation cover?
The presentation should cover your thesis, including motivation, introduction, description of new methods and results of your research. Please do NOT explain already existing methods in detail here, put more focus on novel work and the results.
- What kind of questions will be asked after the presentation?
The questions will be directly connected to your thesis and related theory.
Student Research Projects
[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]
We are always interested in mentoring interesting student research projects. Please contact us directly with an interesting resarch idea. In the future you will also be able to find research project topics below.
Available projects
Currently we are not offering any student research projects.
For more information please visit the official web page Studentische Forschungsprojekte (Lehre@LMU)
Current Theses (With Working Titles)
[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]
| Title | Type |
|---|---|
| Empirical Evaluation of Methods for Discrete Time-to-event Analysis | BA |
| Enhancing stance prediction by utilizing party manifestos | MA |
| Examining and Mitigating Gender Bias in German Word Embeddings | BA |
| Exploring the Effects of Domain Shift on Inferred Topics in Neural and Non-Neural Topic Models | BA |
| Transformer Uncertainty Estimation with Stochastic Attention | MA |
| Transfer Learning of Simulation to Hardware Direction Finding for Indoor Position | MA |
| Reliable Self-supervised Learning for Medical Image Analysis | MA |
| Quantification of Uncertainties via Deep Learning for Medical Image Segmentation | MA |
| Deep Efficient Transformers for Learning Representation of Genomic Sequences | MA |
| Self-Supervised Multimodal Metric Learning | MA |
| Diverse Sentence Embedding for Legal Multi-Label Document Classification | MA |
| Unsupervised Domain Adaptive Object Detection | MA |
| Uncertainty-Aware Self-Supervised Learning | MA |
| Data-driven Lag-lead Selection for Exposure-Lag-Response Associations | BA |
| Probabilistic Deep Learning of Liver Failure in Therapeutical Cancer Treatment | MA |
| Model agnostic Feature Importance by Loss Measures | MA |
| Model-agnostic interpretable machine learning methods for multivariate | MA |
| Time Series Forecasting | MA |
| Normalizing Flows for Interpretablity Measures | MA |
| Representation Learning for Semi-Supervised Genome Sequence Classification | MA |
| Neural Architecture Search for Genomic Sequence Data | MA |
| Comparison of Machine Learning Models For Competing Risks Survival Analysis | MA |
| Multi-accuracy calibration for survival models | MA |
| Interpretation of black box models using tree-based surrogate models | MA |
Completed Theses
[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]
Completed Theses (LMU Munich)
| Title | Type | Completed |
|---|---|---|
| Domain transfer across country, time and modality in multiclass-classification | BA | 2022 |
| Predicted Sentiments of Customer Texts as Covariates for Time Series Forecasting | MA | 2022 |
| Gaussian Process Regression and Bayesian Deep Learning for Insurance Tariff Migration | MA | 2022 |
| Transformer Model for Genome Sequence Analysis | BA | 2022 |
| Self-supervised Representation Learning for Genome Sequence Data | MA | 2022 |
| Self-supervised Learning Framework for Imbalanced Positive-Unlabeled Data | MA | 2022 |
| A comparative Evaluation of the Utility of linguistic Features for Part-of-Speech-Tagging | BA | 2022 |
| Evaluating pre-trained language models on partially unlabeled multilingual economic corpora | MA | 2022 |
| How Different is Stereotypical Bias in Different Languages? Analysis Multilingual Language Models | MA | 2022 |
| Leveraging pairwise constraints for topic discovery in weakly annotated text data | MA | 2022 |
| Word Embedding Evaluation with Intrinsic Evaluators | BA | 2022 |
| Application of neural topic models to twitter data from German politicians | BA | 2022 |
| Visualizing Hyperparameter Performance Dependencies | BA | 2022 |
| Deep Self-Supervised Divergence Learning | MA | 2021 |
| Neural Architecture Search for Genomic Sequence Data | MA | 2021 |
| Multi-state modeling in the context of predictive maintenance | MA | 2021 |
| Multi-state modeling in the context of predictive maintenance | MA | 2021 |
| Model Based Quality Diversity Optimization | MA | 2021 |
| mlr3automl - Automated Machine Learning in R | MA | 2021 |
| Knowledge destillation - Compressing arbitrary learners into a neural net | MA | 2020 |
| Personality Prediction Based on Mobile Gaze and Touch Data | MA | 2020 |
| Identifying Subgroups induced by Interaction Effects | MA | 2020 |
| Benchmarking: Tests and Vizualisations | MA | 2019 |
| Counterfactual Explanations | MA | 2019 |
| Methodik, Anwendungen und Interpretation moderner Benchmark-Studien am Beispiel der | MA | 2019 |
| Risikomodellierung bei akuter Cholangitis | ||
| Machine Learning pipeline search with Bayesian Optimization and Reinforcement Learning | MA | 2019 |
| Visualization and Efficient Replay Memory for Reinforcement Learning | BA | 2019 |
| Neural Network Embeddings for Categorical Data | BA | 2019 |
| Localizing phosphorylation sites by deep learning-based fragment ion intensity | MA | 2019 |
| Average Marginal Effects in Machine Learning | MA | 2019 |
| Wearable-based Severity Detection in the Context of Parkinson’s Disease Using | MA | 2018 |
| Deep Learning Techniques | ||
| Bayesian Optimization under Noise for Model Selection in Machine Learning | MA | 2018 |
| Interpretable Machine Learning - An Application Study using the Munich Rent Index | MA | 2018 |
| Automatic Gradient Boosting | MA | 2018 |
| Efficient and Distributed Model-Based Boosting for Large Datasets | MA | 2018 |
| Linear individual model-agnostic explanations - discussion and empirical analysis of modifications | MA | 2018 |
| Extending Hyperband with Model-Based Sampling Strategies | MA | 2018 |
| Reinforcement learning in R | MA | 2018 |
| Anomaly Detection using Machine Learning Methods | MA | 2018 |
| RNN Bandmatrix | MA | 2018 |
| Configuration of deep neural networks using model-based optimization | MA | 2017 |
| Kernelized anomaly detection | MA | 2017 |
| Automatic model selection amd hyperparameter optimization | MA | 2017 |
| mlrMBO / RF distance based infill criteria | MA | 2017 |
| Kostensensitive Entscheidungsbäume für beobachtungsabhängige Kosten | BA | 2016 |
| Implementation of 3D Model Visualization for Machine Learning | BA | 2016 |
| Eine Simulationsstudie zum Sampled Boosting | BA | 2016 |
| Implementation and Comparison of Stacking Methods for Machine Learning | MA | 2016 |
| Runtime estimation of ML models | BA | 2016 |
| Process Mining: Checking Methods for Process Conformance | MA | 2016 |
| Implementation of Multilabel Algorithms and their Application on Driving Data | MA | 2016 |
| Stability Selection for Component-Wise Gradient Boosting in Multiple Dimensions | MA | 2016 |
| Detecting Future Equipment Failures: Predictive Maintenance in Chemical Industrial Plants | MA | 2016 |
| Fault Detection for Fire Alarm Systems based on Sensor Data | MA | 2016 |
| Laufzeitanalyse von Klassifikationsverfahren in R | BA | 2015 |
| Benchmark Analysis for Machine Learning in R | BA | 2015 |
| Implementierung und Evaluation ergänzender Korrekturmethoden für statistische Lernverfahren | BA | 2014 |
| bei unbalancierten Klassifikationsproblemen |
Completed Theses (Supervised by Bernd Bischl at TU Dortmund)
| Title | Type | Completed |
|---|---|---|
| Anwendung von Multilabel-Klassifikationsverfahren auf Medizingerätestatusreporte zur Generierung von Reparaturvorschlägen | MA | 2015 |
| Erweiterung der Plattform OpenML um Ereigniszeitanalysen | MA | 2015 |
| Modellgestützte Algorithmenkonfiguration bei Feature-basierten Instanzen: Ein Ansatz über das Profile-Expected-Improvement | Dipl. | 2015 |
| Modellbasierte Hyperparameteroptimierung für maschinelle Lernverfahren auf großen Daten | MA | 2015 |
| Implementierung einer Testsuite für mehrkriterielle Optimierungsprobleme | BA | 2014 |
| R-Pakete für Datenmanagement und -manipulation großer Datensätze | BA | 2014 |
| Lokale Kriging-Verfahren zur Modellierung und Optimierung gemischter Parameterräume mit Abhängigkeitsstrukturen | BA | 2014 |
| Kostensensitive Algorithmenselektion für stetige Black-Box-Optimierungsprobleme basierend auf explorativer Landschaftsanalyse | MA | 2013 |
| Exploratory Landscape Analysis für mehrkriterielle Optimierungsprobleme | MA | 2013 |
| Feature-based Algorithm Selection for the Traveling-Salesman-Problem | BA | 2013 |
| Implementierung und Untersuchung einer parallelen Support Vector Machine in R | Dipl. | 2013 |
| Sequential Model-Based Optimization by Ensembles: A Reinforcement Learning Based Approach | Dipl. | 2012 |
| Vorhersage der Verkehrsdichte in Warschau basierend auf dem Traffic Simulation Framework | BA | 2011 |
| Klassifikation von Blutgefäßen und Neuronen des menschlichen Gehirns anhand von ultramikroskopierten 3D-Bilddaten | BA | 2011 |
| Uncertainty Sampling zur Auswahl optimaler Sampler aus der trunkierten Normalverteilung | BA | 2011 |
| Over-/Undersampling für unbalancierte Klassifikationsprobleme im Zwei-Klassen-Fall | BA | 2010 |