Diplom- und Master-Arbeiten (eigene und betreute):

M. Sbardellati:
"Exploratory Visual System for Predictive Machine Learning of Event-Organisation Data";
Betreuer/in(nen): M. Waldner; VVisual Computing and Human-Centered Technology, 2021.

Kurzfassung englisch:
In recent years, the usage of machine learning (ML) models and especially deep neural networks in many different domains has increased rapidly. One of the major challenges when working with ML models is to correctly and efficiently interpret the results given by a model. Additionally, understanding how the model came to its conclusions can be a very complicated task even for domain experts in the field of machine learning. For laypeople, ML models are often just black-boxes. The lack of understanding of a model and its reasoning often leads to users not trusting the model´s predictions. In this thesis, we work with an ML model trained on event-organisation data. The goal is to create an exploratory visual event-organisation system that enables event organisers to efficiently work with the model. The main user goals in this scenario are to maximise profits and to be able to prepare for the predicted number of visitors. To achieve these goals users need to be able to perform tasks like: interpreting the prediction of the current input and performing what-if analyses to understand the effects of changing parameters. The proposed system incorporates adapted versions of multiple state-of-the-art model-agnostic interpretation methods like partial dependence plots and case-based reasoning. Since model-agnostic methods are independent of the ML model, they provide high flexibility. Many state-of-the-art approaches to explain ML models are too complex to be understood by laypeople. Our target group of event organisers cannot be expected to have a sufficient amount of technical knowledge in the field of machine learning. In this thesis, we want to find answers to the questions: How can we visualise ML predictions to laypeople in a comprehensible way? How can predictions be compared against each other? How can we support users in gaining trust in the ML model? Our event-organisation system is created using a human-centred design approach performing multiple case studies with potential users during the whole development circle.

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Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.