Technical University Berlin

Engineering Innovation introduces students to the concepts of innovative thinking and innovation practices. Using lectures, case studies, team exercises and guest speakers, the course teaches life skills in innovative thought and action that students can use in careers ranging from starting companies to executing research and development projects in large companies. Students examine the innovator’s mindset and explore the culture of innovation. In a real- work, hands-on way, students learn how to be innovative and understand why innovation is integral to commercial success in the 21st Century’s digital revolution. Innovation strategies and tactics are evaluated from the perspective of ideation; that is, transforming innovative problem-solving ideas into viable solutions that are produced, sold, consumed, and or implemented in society. Students develop an understanding of the importance of innovation – and how innovation is applied. A best practices approach is used to demonstrate how innovators conceive and implement impactful solutions for a variety of problems. Students learn how technology can serve as both a pathway and a roadblock in organizations committed to operating with an innovator’s mandate. Students are taught practical and applicable skills that can be applied in enterprises ranging from startup ventures to Fortune 100 companies.

The technological and physical basics of Brain-Computer Interfacing will be elaborated. It covers the path from the (electrical) activity of single neurons and networks via the volume conduction of the human head. At the end of the class, students will know the essential physical background of Brain-Computer Interfacing (BCI). They will understand the pathway from the activity of single neurons to the signal of the electroencephalogram (EEG) They will be capable of programming simulations of the electrical properties of the human head as well as simple neural and neural network models.

The lecture covers elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. The course alternates lectures and practice sessions. In the practice sessions, students implement and apply machine learning algorithms on real data in Python. Topics include: supervised learning (linear regression techniques, linear classification, kernel based regression), unsupervised learning (principal component analysis, clustering), and model selection.

The course contains the learning materials, practices and case studies to develop the knowledge and skills of the students in the field of data science and its application in the real business/work world. The students learn how to apply analytical techniques and scientific principles to extract valuable information from business data for decision-making, strategic planning. This course covers practical contents of statistics, machine learning, information visualization, and data analysis techniques through python programming language and other tools.

The course provides an overview over the history of racism from antiquity to the postwar era. It addresses the relationship with different historical developments like colonialism, slavery, race science, eugenics, segregation and genocide. The course discusses the exemplary developments in different European and non-European societies. While the perspective of the victims of racist discrimination is addressed frequently, the course also focuses on the logic of such discrimination. For this, various related issues are raised, like anti-black racism, antisemitism, hatred against Sinti and Roma etc.

This course includes knowledge of common methods in asymmetric encryption, as well as possible attacks in faulty implementations of these methods: RSA, El-Gamal, Diffie-Hellman-Key-Exchange, elliptic curves, and selected methods of Post-Quantum-Cryptography. Students who completed this course possess profound knowledge of cryptographic methods. They are able to correctly and securely use cryptographic protocols. They are proficient in verifying the security of One-Way-Functions and (Pseudo-)Random-Number-Generators. Furthermore, they are able to recognize and avoid typical mistakes in asymmetric encryption.

The course introduces engineering students to the concepts and practices of technological entrepreneurial thinking and entrepreneurship. Using lectures, case studies, business plans, and student presentations, the course teaches life skills in entrepreneurial thought and action that students can utilize in starting technology companies or executing R&D projects in large companies. Major course modules include introduction to entrepreneurship, idea generation and feasibility analysis, and business planning and execution.

Blue Science is a module that allows students to experience interactive university teaching and learning by dealing with their social and ecological responsibility in concrete and active ways. It came up as an transdisciplinary extension of the seminar Blue Engineering, which has been offered for over 10 years, promoting socially and ecologically responsible engineering through a variety of alternative teaching methods. Blue Science is open for students of all areas and backgrounds with an interest in technology. Science and its relation to technology, individuals, nature and society is reflected, analyzed and questioned in interdisciplinary discussions facilitated through diverse methods. Students are encouraged to think and learn independently and creatively, and therefore teacher-centered instruction does not occur in this module. Students acquire the competence to unveil the complex interdependency of their social, political, ecological and economic surroundings. This includes the consideration of different values, interests and needs within a global perspective as well as within one class(room). The course design encourages democratic decision-making not only to solve but also to define problems within the course itself and moreover outside of the classroom. By developing and carrying out their own teaching units, students are actively involved in the teaching process and co-create the course. This allows students to bring in their own areas of interest and to share their findings and points of view with others, creating learning material that may be used in future editions of the module.

When car-use becomes restricted on streets, is that called opening or closing a street? The conflict around this question currently splits Berlin. As the automotive city planning paradigm of much of the twentieth century rolls out bumpily, many are ready to replace it with a paradigm that puts people instead of cars first. The claim is that the urban street of the future is not merely a transit space, but rather part of the urban commons, a space that is there for everyone to use. Climate mitigation, space justice, and mobility justice all play a role here as well. In this seminar, we will look at urban (street) space as urban commons, discuss the change of meaning that street space has undergone in the past and still undergoes and get a first-hand experience of the conflict described above by visiting reclaimed spaces and conducting interviews. We will also deal with specific examples of street reclamations worldwide.

In this course, students are taught the foundational concepts of major stochastic fields and associated topics, including Statistics, probability, and combinatorics. The course is presented in “flipped-classroom” format, such that students are expected to learn concepts on their own, and then practice application in the classroom.