Many applications employ models to represent real-life environments efficiently. To allow these models to be realistic it is commonly fitted using a dataset containing labeled samples. When obtaining a label for a sample from the environment is expensive, it is key that the dataset contains only those samples that aid in providing a realistic model the most. Active Learning (AL) provides searching strategies for selecting these samples based on different heuristics: diversity, informativeness, and representativeness. This thesis focuses specifically on population-based AL for regression, where both sample and output space are infinite. Its goal is to create a performant, efficient, extensible, and generally applicable selection strategy for this setting. To allow for the latter a black-box model, through which its strategy can be used with virtually any model. The strategy itself is modular, allowing for extensions. This strategy iteratively concentrates on an interesting subregion within the sampling space through three modular steps: discretizing the sample space, providing fitness scores to this discretization, and restricting the sample space based on these fitness scores further. This strategy is applied to both a scientific polynomial setting, as well as a car-following setting. Experiments show that this approach outperforms randomly selecting a sample in both cases, especially when a long labeling time is considered.

The majority of the courses in the Computer Science Bachelor at the Delft University of Technology use so called lab sessions to provide an opportunity for students to ask questions about course material and get feedback on their assignment. In order to optimally support the students, teaching assistants, or TAs, are appointed to assist the lecturer during the lab sessions. With the number of students in the Bachelor quickly growing, the process of manually recruiting students to become a TA and assigning the TAs to lab sessions is becoming infeasible.

This project aims to ease the process of gathering and scheduling TAs. In order to achieve this goal, the Teaching Assistant Management platform, or TAM, has been developed. All parties involved in the process of appointing TAs can use TAM to provide their input. Lecturers can register their courses on TAM, students are able to indicate their interest to help with different courses and representatives from Education and Student Affairs can validate the application of the interested students. Using this input, TAM is able to automatically create a schedule by assigning TAs to lab sessions. To provide an algorithm for the automatic generation of schedules, a model based on the minimumcost max flow problem is created. Due to complications with the implementation of the minimum-cost max flow model, the schedule generation is handled by a linear solver: Gurobi. By modeling the constrains for a schedule to be considered valid, Gurobi is used to process the input of the users into an optimized schedule.

TAM consists of three components: a MySQL database, a backend written using Spring, containing the business logic and the implementation of the scheduler, and a frontend website created with Vue to provide an interface to the users. The frontend and the backend are connected using a REST API.

A unique aspect of the project is the live deployment of TAM. At the end of the fourth week, the first version was deployed, allowing interested students to submit their course preferences. Subsequent features have been deployed iteratively. During the development, muliple problems have been encountered. The team underestimated the time required to learn the new technologies, as well as the time needed to maintain a system in production. Furthermore, configuring Single Sign-On required more time than expected.