German

In this class on the A2 level according to CEFR, students learn to understand sentences and frequently used expressions related to areas of most immediate relevance (e.g. basic personal and family information, shopping, local geography, employment). They study to communicate in simple and routine tasks requiring a simple and direct exchange of information on familiar and routine matters. Students work to describe in simple terms aspects of their background, immediate environment, and matters in areas of immediate need. Topics are taken from Berlin and German history and culture and also include politics as well as intercultural topics and current events. The A2 level is split into two consecutive courses, the A2.1 course covers the first half of the level and the A2.2 course covers the second half of the level.

In this course, students review, consolidate, and expand their knowledge of basic German grammar. They practice structures needed in everyday communication.

This lecture conveys the biophysical bases for the description and understanding of the structure, dynamics, and functions of biological molecules. Topics include an introduction to biological macromolecules; structure of complex biomolecules; self-organization of proteins and membranes through hydrophobic forces; ions, protonation, and protein electrostatics; introduction to calculations of molecular mechanics; protein folding and predicting structure; and motor enzymes and nanometer-scale movement.

This course covers the following subjects: representations of numbers and arithmetic error (floating point math), functions and roots, linear and non-linear systems of equations, interpolation and approximative representations of functions, numerical differentiation and integration, ordinary and partial differential equations, eigenvalue problems (wave equations), molecular dynamics simulations (planet systems, Lennard-Jones liquids, molecular chaos), stochastics, Monte-Carlo integration, Monte-Carlo metropolis simulation (lattice spin model), optimization of non-linear problems, steepest descent,  conjugate gradient, simulated annealing (traveling salesman problem), Fourier transforms, spectral analysis (analysis of acoustic signals, audio synthesis), networks, infection models, random walks, reaction-diffusion systems, predator-prey population dynamics, cellular automata (Game of Life), and artificial neural networks.

This course, comprised of a lecture and discussion section, includes the following topics: 1) Introduction (historical notes, coordinate dependence of Newton‘s equations, systems with constraints); 2) Lagrange equations (systems w/o constraints, non-inertial reference frames, constraints and generalized coordinates, virtual displacements, D’Alembert’s principle, systems w/ constraints); 3) Hamilton‘s principle (variational calculus, derivation of Lagrange equations from Hamilton’s principle, Lagrange multipliers and constraints); 4) Symmetries and conservation laws (cyclic coordinates and canonical momenta, translational and rotational invariance, Noether theorem, translational invariance in time and energy conservation, energy conservation in 1D systems, Galilei invariance and Lagrangian of free particles, relativistic mechanics of free particles, gauge invariance, mechanical similarity); 5) Oscillations (coupled oscillators, driven oscillators, Green function of damped oscillator, parametric resonance, motion in rapidly oscillating fields); 6) Rigid bodies (degrees of freedom, tensor of inertia and kinetic energy, angular momentum, principal axes of tensor of inertia, equations of motion, Euler angles, free symmetric top, heavy symmetric top, fast top).

German academic writing is a skill that can be learned. By engaging with selected modern literary texts in the writing lab, students practice to develop research questions, prepare outlines, draft exposés, construct arguments, and comment on academic positions. The goal of the course is to enable participants to prepare well-structured term papers, bachelor's or master's theses, dissertations, and presentations. It also address the grammatical and
stylistic peculiarities of the German academic language, including intercultural distinctions. Moreover, students investigate the promise, perils, and limitations of artificial intelligence (AI), and the extent to which AI can facilitate many areas of academic work but not replace the need for critical and innovative thinking. By the end of the course, participants are equipped to successfully stand their ground in German academic discourse. At the same time, they acquire transferable skills to write clearly structured, concise academic texts in their own language.

Language anxiety and linguistic insecurity are central topics in multilingual and transcultural contexts. In this seminar, students investigate the causes and effects of language anxiety, in language acquisition as well as in the day to day. The class looks at different forms of linguistic insecurity and language anxiety that are affected by social norms, language ideologies, and individual experiences. The goal of the seminar is to develop a critical understanding of this phenomenon and how to approach linguistic insecurity. The readiness to work with research literature in English is required. Students need to take this seminar alongside the lecture "Second Language Acquisition and Multilingualism".

This course is for absolute beginners. This course introduces students to the fundamentals of German grammar, reading, and writing while developing some basic communicative skills. This course teaches students simple structures, lexis and phrases which enables them to communicate in a limited number of common everyday situations in German-speaking countries.

In this course, students gain an integrative understanding of the field of Artificial Intelligence (AI), with equal emphasis on data-driven AI (especially machine learning) and model-based AI (especially planning and reasoning). They come to understand AI from the perspectives of decision theory, machine learning, optimization, and classical problem solving. Students learn to independently implement and understand core algorithms from these areas and can identify appropriate problem formulations and AI algorithms for a given application. Course topics include problem formulations and algorithmic approaches from decision theory (including reinforcement learning, multi-armed bandits, control theory), machine learning, optimization, and inference, classical planning, and problem solving. The class also discusses fundamental and recurring algorithmic principles such as dynamic programming, optimization-based vs. sampling-based methods, and decision trees.