Measuring students’ progress in Machine Learning: A case study of Decision Trees and Random Forests

Machine Learning (ML) is a rapidly growing field, therefore ensuring that students deeply understand such concepts is of key importance in order to certify that they are prepared for the challenges and opportunities of the future workforce. Despite this, literature on teaching ML and assessing students' understanding with regard to this field is...

Optimal Regression Trees via Dynamic Programming: Optimization techniques for learning Regression Trees

Decision trees make decisions in a way interpretable to humans, this is important when machines are increasingly used to aid in making high-stakes and socially sensitive decisions. While heuristics have been used for a long time to find decision trees with reasonable accuracy, recent approaches find fully optimal trees. Due to the computational...

Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...

Virtual Assistant for maintenance budget estimation: Using Machine Learning to improve the objectivity of maintenance budget estimates of civil engineering structures

With an increase of data documentation and standardization in the construction field in The Netherlands, by norms such as the NEN, there is a possibility to introduce data-driven approaches to certain areas within the construction industry. One of these is the area of budget estimation which is currently fully dependent on a cost estimating...

Improving the robustness of decision trees in security-sensitive setting

Machine learning is used for security purposes, to differ between the benign and the malicious. Where decision trees can lead to understandable and explainable classifications, an adversary could manipulate the model input to evade detection, e.g. the malicious been classified as the benign. State-of-the-art techniques improve the robustness by...

Adversarially Robust Decision Trees Against User-Specified Threat Models

In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as decision trees are understandable, they suffer from adversarial attacks. When a decision tree is used to differentiate between a user's benign and malicious behavior, an adversarial attack...