Technical University Berlin

This course consists of practical project work or audit and an evaluation field stay including technologies and background information necessary to develop sustainable community-based projects, e.g. PV training, CO2compensation, household biogas plants, clean cooking, biogas, income generation. International student hybrid working groups develop CO2 compensation projects for climate and SDGs tackling the needs of the local partner communities together with the partner NGOs. The course offers research and innovation opportunities to deepen the development and application of sustainable technologies and methodologies. It also includes cooperation with local community organizations, NGOs, international universities, and other partners.

This course's aim is to develop an understanding of large language models (LLM) as well as their evaluation and to practice reading, understanding, and presenting research work. As part of the class, methods for the selection of data and LLMs, data preparation, application and evaluation of LLMs are developed and put into practice. The use cases are examples from the field of natural language processing, e.g. translation, summary of text, or information extraction.

This course discusses differential geometry of curves and surfaces in Euclidian Space: curves in 2- and 3-dimensional spaces, local and global theory of surfaces, special classes of surfaces, discrete curves and surfaces.

This course starts with the question of what is understood by “gender” in gender studies and a systematization of the field of gender studies in STEM/planning. In the second part students examine studies of gender studies in STEM and planning in different disciplines. The course concludes with a project phase in which students are in working groups on topics from the field of gender studies on STEM/planning using the materials provided and present the results.

All engineering disciplines today employ machine learning for monitoring systems and fault detection, for data-based decision support as well as for leveraging new potentials in the environment of big data. This module teaches the fundamentals of standard machine learning techniques as well as their implementation using standard libraries in the Python programming language based on real-world engineering examples. It focuses on the complete data science process from data exploration over modeling to inference and production.

The course is an introduction to the geometry of the image formation process and how visual data is represented and manipulated in a computer. Students learn projective geometry, which helps model the perspective projection, and digital image processing. Topics include how to model the perspective operation that happens when a picture is taken (projective geometry, image formation process), how pictures (visual data) are represented and processed in a computer (digital image processing), how to find out the internal geometric parameters of a camera (camera calibration), and what applications camera technology has in robotics (stereopsis, visual odometry, AR/VR, etc.).

The course's goal is to enable participants to acquire and process digital images in technical applications in a context-aware manner. The course introduces the basics of digital image processing, the acquisition of images in computing environments, and the extraction of semantic contents from the images. The goal of the course is the exemplary coverage of an interdisciplinary breadth, not necessarily an in-depth treatment of a specific domain. Fundamentals like sensor calibration, feature detection (e.g. edge extraction), matching and classification are taught. Integrated practical exercises cover operating a camera from a single-board computer and using a smartphone camera in a computer vision setting. Furthermore, exemplary machine learning approaches are used for “understanding” the images acquired previously. Software to be developed make use of the OpenCV Python library.