Technical University Berlin

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.

In this course, students gain a fundamental understanding of the functioning of international energy markets and perform sound analyses on energy markets. Students learn about the national and international transport and consumption of the main energy sources. Topics also include external costs and steering instruments, insights into newest developments, and how to do cost accounting and capital budgeting with respect to energy economics. 

The course includes international hybrid workshops by lecturers of all partner countries on technologies and background information necessary to develop sustainable community-based projects. Topics include intercultural communication, PV training, CO2compensation, household biogas plants, clean cooking, biogas, social business; international student hybrid working groups developing CO2compensation projects for climate and SDGs tackling the needs of the local partner communities together with the partner NGOs; practical Service elements contributing to the success of the project for the partner community and to the climate action (including, e.g., training sessions in schools, fundraising events, activities in waste management, organic gardening, tree planting); and research and innovation opportunities to deepen the development and application of sustainable technologies and methodologies.

The lecture covers four major aspects of HCI: 1. Understanding users (user behavior, user research techniques such as interviews and usability testing) 2. Designing user interfaces (principles of interface design for usability, interaction paradigms) 3. Evaluating interfaces (usability testing methods, identifying usability problems, iterative design based on user feedback) 4. Integrating HCI into system development (integrating the above aspects into an iterative product development cycle). The exercise section of the course applies the above theory in practice. Learning outcomes include: Apply HCI principles to design user-friendly interfaces; conduct fundamental user research and analyze user needs; understand principles of iterative prototyping and evaluation of interactive systems; communicate HCI concepts effectively.

This course primarily targets Masters students but also ambitious Bachelor students who want to get the opportunity to broaden their knowledge of specific wireless communication technologies. After completing this course, students will have deep knowledge about wireless technologies from the IEEE 802 protocol family (e.g., WiFi, Bluetooth and ZigBee), technologies for adhoc/mesh networks and classical cellular networks. Additionally, during the labs, students will have the opportunity to study selected technologies or technology-oriented problems in hands-on exercises.

Participants learn basic concepts, their theoretical foundation, and the most common algorithms used in machine learning and artificial intelligence. After completing the module, participants understand strengths and limitations of the different paradigms, are able to correctly and successfully apply methods and algorithms to real world problems, are aware of performance criteria, and are able to critically evaluate results obtained with those methods. More specifically, participants are able to demonstrate: 1) Understanding regarding basic concepts of neural information processing 2) Knowledge of unsupervised machine learning methods 3) Application to problems of statistical modeling, explorative data analysis, and visualization. Topics include 

1) Principal Component Analysis, Kernel-PCA 

2) Independent Component Analysis (Infomax, FastICA, Second Order Blind Source Separation) 

3) Stochastic Optimization 

4) Clustering, Embedding, and Visualisation (Central and Pairwise Clustering, Self-Organizing Maps, Locally Linear Embedding) 

5) Density Estimation, Mixture Models, Expectation-Maximization Algorithm, Hidden Markov Model 

6) Estimation Theory, Maximum Likelihood Estimation, Bayesian Model Comparison