# Theses

The working group typically offers various thesis topics each semester in the areas computational statistics, machine learning, data mining, optimization and statistical software. You’re 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 skills.

Before you start writing your thesis you **must** look for a supervisor within the working group.

Send an email to **janek.thomas [at] stat.uni-muenchen.de** 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 working group.

*For a list of current theses click here. For a list of completed theses click here.*

#### * Learning Embeddings for Categorical Variables (Betreuer: Florian Pfisterer)

Many machine learnings naturally lend themselves to numeric data. In order for them to be able to deal with categorical data, either extensions of the algorithms or numerical representations (one-hot encoding etc.) are required. A class of those numerical representations are so called ‘embeddings’, that can be obtained for example from neural networks. Embeddings can be learned from datasets using different methods. Methods that allow for learning embeddings will be implemented and tested in this thesis.

Possible directions:

- Explorative and Interpretable embeddings for a single dataset (e.g. video game data).
- Embedding as as a general method for encoding categorical variables.

#### * Compressing Ensembles of Machine Learning Models (Betreuer: Florian Pfisterer)

Complex ensembles of machine learning models are usually more performant, but very hard to deploy in real world applications, such as mobile phones, machines etc. The question to be answered in this work, is whether we can compress the results of an ensemble into a single model, that is (possibly) easily deployable with minimal prerequisites and (technical, time-) overhead. Training of NN’s can be simplified, as overfitting on the predictions of the ensemble is no longer a problem, but something to strive for. A possible class of those approximators can be the family of (feed-forward) neural networks. The work includes implementing functionality that allows for training a learner on the output of an arbitrary ensemble / model. Afterwards, an evaluation of the model performance and resulting stability / usability in the proposed context of compression needs to be conducted. This includes comparing different NN architectures with respect to stability, and evaluating possible extensions to the usual training processes, that would allow for faster or more stable training. An additional question is, whether some parts of preprocessing can also be approximated in this way, which would further reduce the overhead required for real world deployment of such models.

#### * Multi-Output Prediction (Betreuer: Quay Au)

The general learning task of predicting multiple targets, which could be real-valued, binary, ordinal, categorical or even of mixed type is known as multi-output prediction. The general idea is to improve the accuracy of a predictor by making use of the statistical dependencies among the output variables. Methods, which transform the multi-output prediction problem into single-output prediction problems, so that ordinary classification and regression algorithms can be applied, shall be implemented in the machine learning R package mlr. The evaluation of multi-output prediction problems, is inherently a challenging task and shall be worked out in this thesis.

#### * Highdimensional Seature Selection (Betreuer: Xudong Sun)

High-dimensional feature selection remains a challenging topic. High-dimensional data include functional data like curve or video data, high-throughput biotechnology data and so on. This project will explore new advances in this field. Ideally, implementation could be done for at least one up-to-date algorithm.

#### * Video Activity Detection Using Convolutional Recurrent Neural Networks (Betreuer: Xudong Sun)

This project will utilize some state of art models in recurrent neural network and convolutional neural network and benchmark the results on some public datasets, for instance UCF101. This is an application of functional on scalar classification extended from the one dimensional curve case to multidimensional image case.

#### * Online Machine Learning Implementation (Betreuer: Xudong Sun)

This project is about implementation of several online machine learning algorithms like online RDA or online boosting. Applicants need a sound understanding of exisiting algorithms and adapt them to online fashion and implement them in R and/or RCPP.

## 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.

For more information please visit the official web pageStudentische Forschungsprojekte (Lehre@LMU)

## Current Theses (With Working Titles)

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

Student | Title | Type |
---|---|---|

J. Moosbauer | Bayesian Optimization under Noise for Model Selection in Machine Learning | MA |

J. Fried | Interpretable Machine Learning - An Application Study using the Munich Rent Index | MA |

B. Burger | Average Marginal Effects in Machine Learning | MA |

J. Goschenhofer | MA | |

S. Gruber | Visualization and Efficient Replay Memory for Reinforcement Learning | BA |

## Completed Theses

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

### Completed Theses (LMU Munich)

Student | Title | Type | Completed |
---|---|---|---|

S. Coors | Automatic Gradient Boosting | MA | 2018 |

D. Schalk | Efficient and Distributed Model-Based Boosting for Large Datasets | MA | 2018 |

K. Engelhardt | Linear individual model-agnostic explanations - discussion and empirical analysis of modifications | MA | 2018 |

N. Klein | Extending Hyperband with Model-Based Sampling Strategies | MA | 2018 |

M. Dumke | Reinforcement learning in R | MA | 2018 |

M. Lee | Anomaly Detection using Machine Learning Methods | MA | 2018 |

J. Langer | RNN Bandmatrix | MA | 2018 |

B. Klepper | Configuration of deep neural networks using model-based optimization | MA | 2017 |

F. Pfisterer | Kernelized anomaly detection | MA | 2017 |

M. Binder | Automatic model selection amd hyperparameter optimization | MA | 2017 |

V. Mayer | mlrMBO / RF distance based infill criteria | MA | 2017 |

L. Haller | Kostensensitive Entscheidungsbäume für beobachtungsabhängige Kosten | BA | 2016 |

B. Zhang | Implementation of 3D Model Visualization for Machine Learning | BA | 2016 |

T. Riebe | Eine Simulationsstudie zum Sampled Boosting | BA | 2016 |

P. Rösch | Implementation and Comparison of Stacking Methods for Machine Learning | MA | 2016 |

M. Erdmann | Runtime estimation of ML models | BA | 2016 |

A.Exterkate | Process Mining: Checking Methods for Process Conformance | MA | 2016 |

J.-Q. Au | Implementation of Multilabel Algorithms and their Application on Driving Data | MA | 2016 |

(J.-Q. Au was a master student of TU Dortmund) | |||

J. Thomas | Stability Selection for Component-Wise Gradient Boosting in Multiple Dimensions | MA | 2016 |

A. Franz | Detecting Future Equipment Failures: Predictive Maintenance in Chemical Industrial Plants | MA | 2016 |

T. Kühn | Fault Detection for Fire Alarm Systems based on Sensor Data | MA | 2016 |

B. Schober | Laufzeitanalyse von Klassifikationsverfahren in R | BA | 2015 |

F. Pfisterer | Benchmark Analysis for Machine Learning in R | BA | 2015 |

T. Kühn | Implementierung und Evaluation ergänzender Korrekturmethoden für statistische Lernverfahren | BA | 2014 |

bei unbalancierten Klassifikationsproblemen |

### Completed Theses (Supervised by Bernd Bischl at TU Dortmund)

Student | Title | Type | Completed |
---|---|---|---|

P. Probst | Anwendung von Multilabel-Klassifikationsverfahren auf Medizingerätestatusreporte zur Generierung von Reparaturvorschlägen | MA | 2015 |

D. Kirchhoff | Erweiterung der Plattform OpenML um Ereigniszeitanalysen | MA | 2015 |

J. Bossek | Modellgestützte Algorithmenkonfiguration bei Feature-basierten Instanzen: Ein Ansatz über das Profile-Expected-Improvement | Dipl. | 2015 |

J. Richter | Modellbasierte Hyperparameteroptimierung für maschinelle Lernverfahren auf großen Daten | MA | 2015 |

B. Elkemann | Implementierung einer Testsuite für mehrkriterielle Optimierungsprobleme | BA | 2014 |

M. Dagge | R-Pakete für Datenmanagement und -manipulation großer Datensätze | BA | 2014 |

K. U. Schorck | Lokale Kriging-Verfahren zur Modellierung und Optimierung gemischter Parameterräume mit Abhängigkeitsstrukturen | BA | 2014 |

P. Kerschke | Kostensensitive Algorithmenselektion für stetige Black-Box-Optimierungsprobleme basierend auf explorativer Landschaftsanalyse | MA | 2013 |

D. Horn | Exploratory Landscape Analysis für mehrkriterielle Optimierungsprobleme | MA | 2013 |

J. Bossek | Feature-based Algorithm Selection for the Traveling-Salesman-Problem | BA | 2013 |

O. Meyer | Implementierung und Untersuchung einer parallelen Support Vector Machine in R | Dipl. | 2013 |

S. Hess | Sequential Model-Based Optimization by Ensembles: A Reinforcement Learning Based Approach | Dipl. | 2012 |

P. Kerschke | Vorhersage der Verkehrsdichte in Warschau basierend auf dem Traffic Simulation Framework | BA | 2011 |

L. Schlieker | Klassifikation von Blutgefäßen und Neuronen des menschlichen Gehirns anhand von ultramikroskopierten 3D-Bilddaten | BA | 2011 |

H. Riedel | Uncertainty Sampling zur Auswahl optimaler Sampler aus der trunkierten Normalverteilung | BA | 2011 |

S. Meinke | Over-/Undersampling für unbalancierte Klassifikationsprobleme im Zwei-Klassen-Fall | BA | 2010 |