# Search result: Catalogue data in Autumn Semester 2018

Computer Science Bachelor | ||||||

First Year Examinations | ||||||

First Year Examination Block 1 | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|

401-0131-00L | Linear Algebra | O | 7 credits | 4V + 2U | Ö. Imamoglu, O. Sorkine Hornung | |

Abstract | Introduction to linear algebra (vector spaces, linear transformations, matrices) , matrix decompositions (LU, QR, eigenvalue and singular value decomposition). | |||||

Objective | Die Lernziele sind: - die fundamentalen Konzepte der linearen Algebra gut zu verstehen und anwenden zu können - Anwendungen der linearen Algebra kennenzulernen | |||||

Content | Linear Algebra: Linear systems of equations, vectors and matrices, norms and scalar products, LU decomposition, vector spaces and linear transformations, least squares problems, QR decomposition, determinants, eigenvalues and eigenvectors, singular value decomposition, applications. | |||||

Lecture notes | Lecture notes "Linear Algebra" (by Gutknecht) in German, with English expressions for all technical terms. | |||||

Prerequisites / Notice | The relevant high school material is reviewed briefly at the beginning. | |||||

252-0025-00L | Discrete Mathematics | O | 7 credits | 4V + 2U | U. Maurer | |

Abstract | Content: Mathematical reasoning and proofs, abstraction. Sets, relations (e.g. equivalence and order relations), functions, (un-)countability, number theory, algebra (groups, rings, fields, polynomials, subalgebras, morphisms), logic (propositional and predicate logic, proof calculi). | |||||

Objective | The primary goals of this course are (1) to introduce the most important concepts of discrete mathematics, (2) to understand and appreciate the role of abstraction and mathematical proofs, and (3) to discuss a number of applications, e.g. in cryptography, coding theory, and algorithm theory. | |||||

Content | See course description. | |||||

Lecture notes | available (in english) | |||||

252-0027-00L | Introduction to Programming | O | 7 credits | 4V + 2U | T. Gross | |

Abstract | Introduction to fundamental concepts of modern programming and operational skills for developing high-quality programs, including large programs as in industry. The course introduces software engineering principles with an object-oriented approach based. | |||||

Objective | Many people can write programs. The "Introduction to Programming" course goes beyond that basic goal: it teaches the fundamental concepts and skills necessary to perform programming at a professional level. As a result of successfully completing the course, students master the fundamental control structures, data structures, reasoning patterns and programming language mechanisms characterizing modern programming, as well as the fundamental rules of producing high-quality software. They have the necessary programming background for later courses introducing programming skills in specialized application areas. | |||||

Content | Basics of object-oriented programming. Objects and classes. Pre- and postconditions, class invariants, Design by Contract. Fundamental control structures. Assignment and References. Basic hardware concepts. Fundamental data structures and algorithms. Recursion. Inheritance and interfaces, introduction to event-driven design and concurrent programming. Basic concepts of Software Engineering such as the software process, specification and documentation, reuse and quality assurance. | |||||

Lecture notes | The lecture slides are available for download on the course page. | |||||

Literature | See the course page for up-to-date information. | |||||

Prerequisites / Notice | There are no special prerequisites. Students are expected to enroll in the other courses offered to first-year students of computer science. | |||||

252-0026-00L | Algorithms and Data Structures | O | 7 credits | 3V + 2U + 1A | M. Püschel, D. Steurer | |

Abstract | This course is about fundamental algorithm design paradigms, classic algorithmic problems, and data structures. The connection between algorithms and data structures is explained for geometric and graph problems. For this purpose, fundamental graph theoretic concepts are introduced. | |||||

Objective | An understanding of the design and analysis of fundamental algorithms and data structures. | |||||

Content | Es werden grundlegende Algorithmen und Datenstrukturen vorgestellt und analysiert. Dazu gehören auf der einen Seite Entwurfsmuster für Algorithmen, wie Induktion, divide-and-conquer, backtracking und dynamische Optimierung, ebenso wie klassische algorithmische Probleme, wie Suchen und Sortieren. Auf der anderen Seite werden Datenstrukturen für verschiedene Zwecke behandelt, darunter verkettete Listen, Hashtabellen, balancierte Suchbäume, verschiedene heaps und union-find-Strukturen. Weiterhin wird Adaptivität bei Datenstrukturen (wie etwa Splay-Bäume) und bei Algorithmen (wie etwa online-Algorithmen) beleuchtet. Das Zusammenspiel von Algorithmen und Datenstrukturen wird anhand von Geometrie- und Graphenproblemen illustriert. Hierfür werden grundlegende Konzepte der Graphentheorie eingeführt. | |||||

Literature | Th. Ottmann, P.Widmayer: Algorithmen und Datenstrukturen, Spektrum-Verlag, 5. Auflage, Heidelberg, Berlin, Oxford, 2011 | |||||

First Year Examination Block 2 Offered in the spring semester. | ||||||

Basic Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0057-00L | Theoretical Computer Science | O | 7 credits | 4V + 2U | J. Hromkovic, H.‑J. Böckenhauer | |

Abstract | Concepts to cope with: a) what can be accomplished in a fully automated fashion (algorithmically solvable) b) How to measure the inherent difficulty of tasks (problems) c) What is randomness and how can it be useful? d) What is nondeterminism and what role does it play in CS? e) How to represent infinite objects by finite automata and grammars? | |||||

Objective | Learning the basic concepts of computer science along their historical development | |||||

Content | This lecture gives an introduction to theoretical computer science, presenting the basic concepts and methods of computer science in its historical context. We present computer science as an interdisciplinary science which, on the one hand, investigates the border between the possible and the impossible and the quantitative laws of information processing, and, on the other hand, designs, analyzes, verifies, and implements computer systems. The main topics of the lecture are: - alphabets, words, languages, measuring the information content of words, representation of algorithmic tasks - finite automata, regular and context-free grammars - Turing machines and computability - complexity theory and NP-completeness - design of algorithms for hard problems | |||||

Lecture notes | The lecture is covered in detail by the textbook "Theoretical Computer Science". | |||||

Literature | Basic literature: 1. J. Hromkovic: Theoretische Informatik. 5th edition, Springer Vieweg 2014. 2. J. Hromkovic: Theoretical Computer Science. Springer 2004. Further reading: 3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997 4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Introduction to Automata Theory, Languages, and Computation (3rd Edition), Addison-Wesley 2006. 5. I. Wegener: Theoretische Informatik. Teubner. More exercises and examples in: 6. A. Asteroth, Ch. Baier: Theoretische Informatik | |||||

Prerequisites / Notice | During the semester, two non-obligatory test exams will be offered. | |||||

252-0061-00L | Systems Programming and Computer Architecture | O | 7 credits | 4V + 2U | T. Roscoe | |

Abstract | Introduction to systems programming. C and assembly language, floating point arithmetic, basic translation of C into assembler, compiler optimizations, manual optimizations. How hardware features like superscalar architecture, exceptions and interrupts, caches, virtual memory, multicore processors, devices, and memory systems function and affect correctness, performance, and optimization. | |||||

Objective | The course objectives are for students to: 1. Develop a deep understanding of, and intuition about, the execution of all the layers (compiler, runtime, OS, etc.) between programs in high-level languages and the underlying hardware: the impact of compiler decisions, the role of the operating system, the effects of hardware on code performance and scalability, etc. 2. Be able to write correct, efficient programs on modern hardware, not only in C but high-level languages as well. 3. Understand Systems Programming as a complement to other disciplines within Computer Science and other forms of software development. This course does not cover how to design or build a processor or computer. | |||||

Content | This course provides an overview of "computers" as a platform for the execution of (compiled) computer programs. This course provides a programmer's view of how computer systems execute programs, store information, and communicate. The course introduces the major computer architecture structures that have direct influence on the execution of programs (processors with registers, caches, other levels of the memory hierarchy, supervisor/kernel mode, and I/O structures) and covers implementation and representation issues only to the extend that they are necessary to understand the structure and operation of a computer system. The course attempts to expose students to the practical issues that affect performance, portability, security, robustness, and extensibility. This course provides a foundation for subsequent courses on operating systems, networks, compilers and many other courses that require an understanding of the system-level issues. Topics covered include: machine-level code and its generation by optimizing compilers, address translation, input and output, trap/event handlers, performance evaluation and optimization (with a focus on the practical aspects of data collection and analysis). | |||||

Lecture notes | - C programmnig - Integers - Pointers and dynamic memory allocation - Basic computer architecture - Compiling C control flow and data structures - Code vulnerabilities - Implementing memory allocation - Linking - Floating point - Optimizing compilers - Architecture and optimization - Caches - Exceptions - Virtual memory - Multicore - Devices | |||||

Literature | The course is based in part on "Computer Systems: A Programmer's Perspective" (3rd Edition) by R. Bryant and D. O'Hallaron, with additional material. | |||||

Prerequisites / Notice | 252-0029-00L Parallel Programming 252-0028-00L Design of Digital Circuits | |||||

401-0213-16L | Analysis II | O | 5 credits | 2V + 2U | E. Kowalski | |

Abstract | Differential and Integral calculus in many variables, vector analysis. | |||||

Objective | Differential and Integral calculus in many variables, vector analysis. | |||||

Content | Differential and Integral calculus in many variables, vector analysis. | |||||

Literature | Für allgemeine Informationen, sehen Sie bitte die Webseite der Vorlesung: https://metaphor.ethz.ch/x/2017/hs/401-0213-16L/ | |||||

401-0663-00L | Numerical Methods for CSE | O | 8 credits | 4V + 2U + 1P | R. Alaifari | |

Abstract | The course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++. | |||||

Objective | * Knowledge of the fundamental algorithms in numerical mathematics * Knowledge of the essential terms in numerical mathematics and the techniques used for the analysis of numerical algorithms * Ability to choose the appropriate numerical method for concrete problems * Ability to interpret numerical results * Ability to implement numerical algorithms afficiently | |||||

Content | 1. Direct Methods for linear systems of equations 2. Least Squares Techniques 3. Data Interpolation and Fitting 4. Filtering Algorithms 8. Approximation of Functions 9. Numerical Quadrature 10. Iterative Methods for non-linear systems of equations 11. Single Step Methods for ODEs 12. Stiff Integrators | |||||

Lecture notes | Lecture materials (PDF documents and codes) will be made available to the participants through the course web page: https://metaphor.ethz.ch/x/2018/hs/401-0663-00L/ | |||||

Literature | U. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011. A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000. W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006. M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002 P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002 | |||||

Prerequisites / Notice | The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves. | |||||

Core Courses | ||||||

Major: Information and Data Processing | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0206-00L | Visual Computing | O | 8 credits | 4V + 3U | M. Pollefeys, S. Coros | |

Abstract | This course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow. | |||||

Objective | This course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia and computer vision. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH. | |||||

Content | Course topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression. In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms. | |||||

Lecture notes | A scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks. | |||||

Literature | Markus Gross: Computer Graphics, scriptum, 1994-2005 | |||||

Major: Theoretical Computer Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0209-00L | Algorithms, Probability, and Computing | O | 8 credits | 4V + 2U + 1A | E. Welzl, M. Ghaffari, A. Steger, D. Steurer, P. Widmayer | |

Abstract | Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction). | |||||

Objective | Studying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory. | |||||

Lecture notes | Will be handed out. | |||||

Literature | Introduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest; Randomized Algorithms by R. Motwani und P. Raghavan; Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf. | |||||

Major: Systems and Software Engineering | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0217-00L | Computer Systems | O | 8 credits | 4V + 2U + 1A | T. Roscoe, R. Wattenhofer | |

Abstract | This course is about real computer systems, and the principles on which they are designed and built. We cover both modern OSes and the large-scale distributed systems that power today's online services. We illustrate the ideas with real-world examples, but emphasize common theoretical results, practical tradeoffs, and design principles that apply across many different scales and technologies. | |||||

Objective | The objective of the course is for students to understand the theoretical principles, practical considerations, performance tradeoffs, and engineering techniques on which the software underpinning almost all modern computer systems is based, ranging from single embedded systems-on-chip in mobile phones to large-scale geo-replicated groups of datacenters. By the end of the course, students should be able to reason about highly complex, real, operational software systems, applying concepts such as hierarchy, modularity, consistency, durability, availability, fault-tolerance, and replication. | |||||

Content | This course subsumes the topics of both "operating systems" and "distributed systems" into a single coherent picture (reflecting the reality that these disciplines are highly converged). The focus is system software: the foundations of modern computer systems from mobile phones to the large-scale geo-replicated data centers on which Internet companies like Amazon, Facebook, Google, and Microsoft are based. We will cover a range of topics, such as: scheduling, network protocol stacks, multiplexing and demultiplexing, operating system structure, inter-process communication, memory managment, file systems, naming, dataflow, data storage, persistence, and durability, computer systems performance, remove procedure call, consensus and agreement, fault tolerance, physical and logical clocks, virtualization, and blockchains. The format of the course is a set of about 25 topics, each covered in a lecture. A script will be published online ahead of each lecture, and the latter will consist of an interactive elaboration of the material in the script. There is no book for the course, but we will refer to books and research papers throughout to provide additional background and explanation. | |||||

Prerequisites / Notice | We will assume knowlege of the "Systems Programming" and "Computer Networks" courses (or equivalent), and their prerequisites, and build upon them. | |||||

Electives Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

151-0107-20L | High Performance Computing for Science and Engineering (HPCSE) I | W | 4 credits | 4G | P. Koumoutsakos | |

Abstract | This course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering. | |||||

Objective | Introduction to HPC for scientists and engineers Fundamental of: 1. Parallel Computing Architectures 2. MultiCores 3. ManyCores | |||||

Content | Parallel Programming models and languages (OpenMP, MPI). Parallel Performance metrics and Code Optimization. Examples based on grid and particle methods for solving Partial Differential Equations and on fundamentals of stochastic optimisation and machine learning. | |||||

Lecture notes | http://www.cse-lab.ethz.ch/teaching/hpcse-i_hs18/ Class notes, handouts | |||||

252-3110-00L | Human Computer Interaction Number of participants limited to 150. | W | 4 credits | 2V + 1U | O. Hilliges | |

Abstract | The course provides an introduction to the field of human-computer interaction, emphasising the central role of the user in system design. Through detailed case studies, students will be introduced to different methods used to analyse the user experience and shown how these can inform the design of new interfaces, systems and technologies. | |||||

Objective | The goal of the course is that students should understand the principles of user-centred design and be able to apply these in practice. | |||||

Content | The course will introduce students to various methods of analysing the user experience, showing how these can be used at different stages of system development from requirements analysis through to usability testing. Students will get experience of designing and carrying out user studies as well as analysing results. The course will also cover the basic principles of interaction design. Practical exercises related to touch and gesture-based interaction will be used to reinforce the concepts introduced in the lecture. To get students to further think beyond traditional system design, we will discuss issues related to ambient information and awareness. | |||||

227-0627-00L | Applied Computer Architecture | W | 6 credits | 4G | A. Gunzinger | |

Abstract | This lecture gives an overview of the requirements and the architecture of parallel computer systems, performance, reliability and costs. | |||||

Objective | Understand the function, the design and the performance modeling of parallel computer systems. | |||||

Content | The lecture "Applied Computer Architecture" gives technical and corporate insights in the innovative Computer Systems/Architectures (CPU, GPU, FPGA, special processors) and their real implementations and applications. Often the designs have to deal with technical limits. Which computer architecture allows the control of the over 1000 magnets at the Swiss Light Source (SLS)? Which architecture is behind the alarm center of the Swiss Railway (SBB)? Which computer architectures are applied for driver assistance systems? Which computer architecture is hidden behind a professional digital audio mixing desk? How can data streams of about 30 TB/s, produced by a protone accelerator, be processed in real time? Can the weather forecast also be processed with GPUs? How can a good computer architecture be found? Which are the driving factors in succesful computer architecture design? | |||||

Lecture notes | Script and exercices sheets. | |||||

Prerequisites / Notice | Prerequisites: Basics of computer architecture. | |||||

227-1037-00L | Introduction to Neuroinformatics | W | 6 credits | 2V + 1U | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |

Abstract | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||

Objective | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||

Content | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||

Seminar Students may also choose seminars from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these seminars. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-2600-05L | Software Engineering Seminar Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | M. Vechev, D. Drachsler Cohen | |

Abstract | The course is an introduction to research in software engineering, based on reading and presenting high quality research papers in the field. The instructor may choose a variety of topics or one topic that is explored through several papers. | |||||

Objective | The main goals of this seminar are 1) learning how to read and understand a recent research paper in computer science; and 2) learning how to present a technical topic in computer science to an audience of peers. | |||||

Content | The technical content of this course falls into the general area of software engineering but will vary from semester to semester. | |||||

252-3300-00L | Seminar on Computer Architecture The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | O. Mutlu | |

Abstract | In this seminar course, we will cover fundamental and cutting-edge research papers in computer architecture. The course will consist of multiple components that are aimed at improving students' technical skills in computer architecture, critical thinking and analysis on computer architecture concepts, as well as technical presentation of concepts and papers in both spoken and written forms. | |||||

Objective | The main objective is to learn how to rigorously analyze and present papers and ideas computer architecture. We will have rigorous presentation and discussion of selected papers during lectures and a written report delivered by each student at the end of the semester. This course is for those interested in computer architecture. Registered students are expected to attend every lecture and participate in the discussion. | |||||

Content | Topics will center around computer architecture. We will, for example, discuss papers on hardware security; architectural acceleration mechanisms for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; processing inside memory; various fundamental and emerging paradigms in computer architecture; hardware/software co-design and cooperation; fault tolerance; energy efficiency; heterogeneous and parallel systems; new execution models, etc. | |||||

Lecture notes | All required materials will be posted on the course website, location to be determined. | |||||

Literature | Key papers and articles, on both fundamentals and cutting-edge topics in computer architecture will be provided and discussed. These will be posted on the course website. | |||||

Prerequisites / Notice | Design of Digital Circuits. Students should have done very well in Design of Digital Circuits and show a genuine interest in Computer Architecture. | |||||

252-4230-00L | Advanced Algorithms and Data Structures Number of participants limited to 24. As a prerequisite, students must have more than just basic knowledge on algorithms and data structures. If you have enjoyed the class "Algorithms, Probability and Computing", this seminar is just right for you! The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. Takes place for the last time! | W | 2 credits | 2S | P. Widmayer, S. Leucci, P. Uznanski | |

Abstract | We will look into modern approaches of algorithms and data structures. A few breakthrough and highly influential papers from the general area of algorithms, from the past 20 years will be selected for students to study. | |||||

Objective | Develop an understanding of modern techniques and paradigms in the design of algorithms and data structures. | |||||

Content | Topics include (but are not exhausted by): -graph algorithms, -text algorithms, -approximation algorithms, -algebra in algorithms, -streaming algorithms, -conditional lower bounds, -sparsification, -randomness in algorithms, -sampling. | |||||

Prerequisites / Notice | Algorithms and Data Structures, or equivalent. | |||||

Minor Courses | ||||||

3. Semester | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

151-3217-00L | Coaching Students (Basic Training) | W | 1 credit | 1G | R. P. Haas, B. Volk | |

Abstract | Aim is enhancement of knowledge and competency regarding coaching skills. Participants should be active coaches of a student team. Topics: Overview of the roles and mind set of a coach as, introduction into coaching methodology, mutual learning and reflecting of participants coaching expertise and situations. | |||||

Objective | - Basic knowledge about role and mindset of a coach - Basic Knowledge and reflection about classical coaching situations - Inspiration and mutual learning from real coaching sessions (mutual peer observation) | |||||

Content | Basic knowledge about role and mindset of a coach - Introduction into coaching: definition & models - Introduction into the coaching process and team building phases - Role of coaches between examinator, tutor and ""friend"" First steps building up personal coaching competencies, i.e. active listening, asking questions, giving feedback - Competencies in theoretical models - Coaching competencies: exercises and reflection Some Reflection and exchange of experiences about personal coaching situations - Exchange of experiences in the lecture group - Mutual peer observations | |||||

Lecture notes | Slides, script and other documents will be distributed electronically (access only for participants registered to this course) | |||||

Literature | Please refer to lecture script. | |||||

Prerequisites / Notice | Participants (Students, PhD Students, Postdocs) should be actively coaching students. | |||||

227-0945-00L | Cell and Molecular Biology for Engineers IThis course is part I of a two-semester course. | W | 3 credits | 2G | C. Frei | |

Abstract | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||

Objective | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||

Content | Lectures will include the following topics (part I and II): DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells. In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade. | |||||

Lecture notes | Scripts of all lectures will be available. | |||||

Literature | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter. |

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