Search result: Catalogue data in Spring Semester 2020

Computer Science Bachelor Information
Minor Courses
NumberTitleTypeECTSHoursLecturers
151-0854-00LAutonomous Mobile Robots Information W5 credits4GR. Siegwart, M. Chli, N. Lawrance
AbstractThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
ObjectiveThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
Lecture notesThis lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
LiteratureThis lecture is based on the Textbook:
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
227-0075-00LElectrical Engineering IW3 credits2V + 2UJ. Leuthold
AbstractBasic course in electrical engineering with the following topics: Concepts of voltage and currents; Analyses of dc and ac networks; Series and parallel resistive circuits, circuits including capacitors and inductors; Kirchhoff's laws and other network theorems; Transient responses; Basics of electrical and magnetic fields;
ObjectiveUnderstanding of the basic concepts in electrical engineering with focus on network theory. The successful student knows the basic components of electrical circuits and the network theorems after attending the course.
ContentDiese Vorlesung vermittelt Grundlagenkenntnisse im Fachgebiet Elektrotechnik. Ausgehend von den grundlegenden Konzepten der Spannung und des Stroms wird die Analyse von Netzwerken bei Gleich- und Wechselstrom behandelt. Dabie werden folgende Themen behandelt:
Kapitel 1 Das elektrostatische Feld
Kapitel 2 Das stationäre elektrische Strömungsfeld
Kapitel 3 Einfache elektrische Netzwerke
Kapitel 4 Halbleiterbauelemente (Dioden, der Transistor)
Kapitel 5 Das stationäre Magnetfeld
Kapitel 6 Das zeitlich veränderliche elektromagnetische Feld
Kapitel 7 Der Übergang zu den zeitabhängigen Strom- und Spannungsformen
Kapitel 8 Wechselspannung und Wechselstrom
Lecture notesDie Vorlesungsfolien werden auf Moodle bereitgestellt.
Als ausführliches Skript wird das Buch "Manfred Albach. Elektrotechnik, Person Verlag, Ausgabe vom 1.8.2011" empfohlen.
LiteratureFür das weitergehende Studium werden in der Vorlesung verschiedene Bücher vorgestellt.
227-0123-00LMechatronicsW6 credits4GT. M. Gempp
AbstractIntroduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications.
ObjectiveIntroduction into the basics and technology of mechatronical devices. Theoretical and practical know-how of the basic elements of a mechatronical system.
ContentIntroduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications.
Lecture notesRecommendation of textbook. Additional documentation to the individual topics. Documentation from industrial companies.
227-0707-00LOptimization Methods for EngineersW3 credits2GP. Leuchtmann
AbstractFirst half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc.
Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest.
ObjectiveNumerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems.
ContentTypical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes.
Lecture notesPDF of a short skript (39 pages) plus the view graphs are provided
Prerequisites / NoticeLecture only in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester.
227-0803-00LEnergy, Resources, Environment: Risks and ProspectsW6 credits4GO. Zenklusen, T. Flüeler
AbstractMultidisciplinary, interactive course focussing on current debates around environmental and energy issues. Topics include: energy transition, nuclear energy and climate change, 2000-Watt-Society. Concepts such as risk, sustainable development and eco-efficiency are applied to case studies. The course is designed for a pluridisciplinary audience and provides a training ground for critical thinking.
ObjectiveDevelop capacities for explicating environmental problems, for scrutinising proposed solutions and for contributing to debates. Analyse complex issues from different perspectives and using a variety of analytical concepts. Understand interactions between the environment, science and technology, society and the economy. Develop skills in critical thinking, scientific writing and presenting.
ContentFollowing a multidisciplinary outline of current issues in environmental and energy policy, the course introduces theoretical and analytical approaches including "risk", "sustainability", "resource management", "messy problems" as well as concepts from institutional design and environmental economics. Large parts of the course are dedicated to case studies and contributions from participants. These serve for applying concepts to concrete challenges and debates. Topics may include: energy transition, innovation, carbon markets, the future of nuclear energy, climate change and development policy, dealing with disaster risk, the use of non-renewable resources, as well as visions such as 2000-watt society.
Lecture notesPresentations and reader provided in electronic formats.
LiteratureReader provided in electronic formats.
Prerequisites / Notice-
227-0945-10LCell and Molecular Biology for Engineers II
This course is part II of a two-semester course.
Knowledge of part I is required.
W3 credits2GC. Frei
AbstractThe 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.
ObjectiveAfter 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.
ContentLectures will include the following topics: 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 notesScripts of all lectures will be available.
Literature"Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter.
227-1046-00LComputer Simulations of Sensory Systems Information
Does not take place this semester.
W3 credits3G
AbstractThis course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.
ObjectiveOur sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system.
The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses.
In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!!
ContentThe following topics will be covered:
• Introduction into the signal processing in nerve cells.
• Introduction into Python.
• Simplified simulation of nerve cells (Hodgkins-Huxley model).
• Description of the auditory system, including the application of Fourier transforms on recorded sounds.
• Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing.
• Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).
Lecture notesFor each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under Link
LiteratureOpen source information is available as wikibook Link

For good overviews I recommend:

• Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth
ISBN 0071390111 / 9780071390118
THE standard textbook on neuroscience.

• L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702].
This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems.

• G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.

• The best place to get started with Python programming are the Link
Prerequisites / Notice• Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week).
• In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems.
252-4900-00LDidactic Basics for Student Teaching Assistants @ ETHW1 creditG. Serafini
AbstractDidaktische Ausbildung für Hilfsassistierende
ObjectiveDie Lehrassistierenden…

- geben sich gegenseitig Feedback auf ihre Lehre und reflektieren ihre Unterrichtspraxis.

- verstehen die Grundlagen von Lehre und Lernen im Kontext ihres Unterrichtsfachs.

- fühlen sich sicher, aktivierende Lehrmethoden in ihrer Unterrichtspraxis anzuwenden.
Content- Theoretische Grundlagen

- Peerhospitation - Kollegialer Unterrichtsbesuch mit Feedback

- Transferveranstaltung - Was nehme ich aus der Ausbildung, der Peerhospitation und der konkreten Unterrichtserfahrung mit?
351-0578-00LIntroduction to Economic Policy
Does not take place this semester.
W2 credits2V
AbstractFirst approach to the theory of economic policy.
ObjectiveFirst approach to the theory of economic policy.
ContentWirtschaftspolitik ist die Gesamtheit aller Massnahmen von staatlichen Institutionen mit denen das Wirtschaftsgeschehen geregelt und gestaltet wird. Die Vorlesung bietet einen ersten Zugang zur Theorie der Wirtschaftspolitik.

Gliederung der Vorlesung:

1.) Wohlfahrtsökonomische Grundlagen: Wohlfahrtsfunktion, Pareto-Optimalität, Wirtschaftspolitik als Mittel-Zweck-Analyse u.a.

2.) Wirtschaftsordnungen: Geplante und ungeplante Ordnung
3.) Wettbewerb und Effizienz: Hauptsätze der Wohlfahrtsökonomik, Effizienz von Wettbewerbsmärkten
4.) Wettbewerbspolitik: Sicherstellung einer wettbewerblichen Ordnung

Gründe für Marktversagen:
5.) Externe Effekte
6.) Öffentliche Güter
7.) Natürliche Monopole
8.) Informationsasymmetrien
9.) Anpassungskosten
10.) Irrationalität

11.) Wirtschaftspolitik und Politische Ökonomie

Die Vorlesung beinhaltet Anwendungsbeispiele und Exkurse, um eine Verbindung zwischen Theorie und Praxis der Wirtschaftspolitik herzustellen. Z. B. Verteilungseffekte von wirtschaftspolitischen Massnahmen, Kartellpolitik am Ölmarkt, Internalisierung externer Effekte durch Emissionshandel, moralisches Risiko am Finanzmarkt, Nudging, zeitinkonsistente Präferenzen im Bereich der Gesundheitspolitik
Lecture notesNein.
351-0778-00LDiscovering Management
Entry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC.
This course can be complemented with Discovering Management (Excercises) 351-0778-01L.
W3 credits3GL. De Cuyper, S. Brusoni, B. Clarysse, S. Feuerriegel, V. Hoffmann, T. Netland, G. von Krogh
AbstractDiscovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. The module provides an overview of the principles of management, teaches knowledge about management that is highly complementary to the students' technical knowledge, and provides a basis for advancing the knowledge of the various subjects offered at D-MTEC.
ObjectiveThe objective of this course is to introduce the students to the relevant topics of the management literature and give them a good introduction in entrepreneurship topics too. The course is a series of lectures on the topics of strategy, innovation, marketing, corporate social responsibility, and productions and operations management. These different lectures provide the theoretical and conceptual foundations of management. In addition, students are required to work in teams on a project. The purpose of this project is to analyse the innovative needs of a large multinational company and develop a business case for the company to grow.
ContentDiscovering Management aims to broaden the students' understanding of the principles of business management, emphasizing the interdependence of various topics in the development and management of a firm. The lectures introduce students not only to topics relevant for managing large corporations, but also touch upon the different aspects of starting up your own venture. The lectures will be presented by the respective area specialists at D-MTEC.
The course broadens the view and understanding of technology by linking it with its commercial applications and with society. The lectures are designed to introduce students to topics related to strategy, corporate innovation, corporate social responsibility, and business model innovation. Practical examples from industry will stimulate the students to critically assess these issues.
Prerequisites / NoticeDiscovering Management is designed to suit the needs and expectations of Bachelor students at all levels as well as Master and PhD students not belonging to D-MTEC. By providing an overview of Business Management, this course is an ideal enrichment of the standard curriculum at ETH Zurich.
No prior knowledge of business or economics is required to successfully complete this course.
351-0778-01LDiscovering Management (Exercises)
Complementary exercises for the module Discovering Managment.

Prerequisite: Participation and successful completion of the module Discovering Management (351-0778-00L) is mandatory.
W1 credit1UB. Clarysse
AbstractThis course is offered complementary to the basis course 351-0778-00L, "Discovering Management". The course offers an additional exercise in the form of a project conducted in team.
ObjectiveThis course is offered to complement the course 351-0778-00L. The course offers an additional exercise to the more theoretical and conceptual content of Discovering Management.

While Discovering Management offers an introduction to various management topics, in this course, creative skills will be trained by the business game exercise. It is a participant-centered, team-based learning activity, which provides students with the opportunity to place themselves in the role of Chief Innovation Officer of a large multinational company.
ContentAs the students learn more about the specific case and identify the challenge they are faced with, they will have to develop an innovative business case for this multinational corporation. Doing so, this exercise will provide an insight into the context of managerial problem-solving and corporate innovation, and enhance the students' appreciation for the complex tasks companies and managers deal with. The exercise presents a realistic model of a company and provides a valuable learning platform to integrate the increasingly important development of the skills and competences required to identify entrepreneurial opportunities, analyse the future business environment and successfully respond to it by taking systematic decisions, e.g. critical assessment of technological possibilities.
363-1038-00LSustainability Start-Up Seminar Restricted registration - show details
Number of participants limited to 30.
W3 credits2GA.‑K. Zobel, A. H. Sägesser
AbstractExperts lead participants through a venturing process inspired by Lean and Design Thinking methodologies. The course contains problem identification, idea generation and evaluation, team formation, and the development of one entrepreneurial idea per team. A special focus is put on sustainability, in particular on climate change and renewable energy technologies specifically.
Objective1. Students have experienced and know how to take the first steps towards co-creating a venture and potentially company
2. Students reflect deeply on sustainability issues (with a focus on climate change & energy) and can formulate a problem statement
3. Students believe in their ability to bring change to the world with their own ideas
4. Students are able to apply entrepreneurial practices such as the lean startup approach
5. Students have built a first network and know how to proceed and who to approach in case they would like to take their ventures further.
ContentThis course is aimed at people with a keen interest to address sustainability issues (with a focus on climate change and renewable energy), with a curious mindset, and potentially first entrepreneurial ideas!

The seminar consists of a mix of lectures, workshops, individual working sessions, teamwork, and student presentations/pitches. This class will be co-taught by an academic expert (studying innovation, entrepreneurship, and sustainability) and an entrepreneurship and sustainability “practitioner”. Real-world climate entrepreneurs and experts from the Swiss start-up and sustainability community will be invited to support individual sessions.

All course content is based on latest international entrepreneurship practices.

The seminar starts with an introduction to sustainability (with a special focus on climate change & energy) and entrepreneurship. Students are asked to self-select into an area of their interest in which they will develop entrepreneurial ideas throughout the course.

The first part of the course then focuses on deeply understanding sustainability problems within the area of interest. Through workshops and self-study, students will identify key design challenges, generate ideas, as well as provide systematic and constructive feedback to their peers.

In the second part of the course, students will form teams around their generated ideas. In these teams they will develop a business model and, following the lean start-up process, conduct real-life testing, as well as pivoting of these business models.

In the final part of the course, students present their insights gained from the lean start-up process, as well as pitch their entrepreneurial ideas and business models to an expert jury. The course will conclude with a session that provides students with a network and resources to further pursue their entrepreneurial journey.
Lecture notesAll material will be made available to the participants.
Prerequisites / NoticePrerequisite:
Interest in sustainability & entrepreneurship.

Notes:
1. It is not required that participants already have a business idea at the beginning of the course.
2. No legal entities (e.g. GmbH, Association, AG) need to be founded for this course.

Target participants:
PhD students, Msc students and MAS students from all departments. The number of participants is limited to max.30.

Waiting list:
After subscribing you will be added to the waiting list.
The lecturers will contact you a few weeks before the start of the seminar to confirm your interest and to ensure a good mixture of study backgrounds, only then you're accepted to the course.
363-1122-00LFrom Entrepreneurial Thinking to Market Relevance - How Startups Scale Restricted registration - show details
Number of participants limited to 40.
W3 credits2GA. Sethi
AbstractThis elective is relevant if you’re planning to join or start a startup in the near future. It will help you recognise how value is created and captured. This includes go-to market, marketing & visibility across verticals & across the supply chain for sustained value capture & business model sustainability.

In short, it’s the journey of how to create a billion dollar startup.
ObjectiveAt the conclusion of the course, the students are able to:

1. The difference between technology and market relevance
2. Recognise challenges that startups face when they move from technology to commercialisation
3. Addressing the failures of startups in scaling, and how early decisions limit scaling and value capture
4. How recognising market need can help startups to create value and strengthen valuation with investors
ContentTechnology startups face challenges in identifying market relevance in the course of commercialisation. Additionally, once they have matched their offering with market needs, they face additional challenges when scaling up since they get locked in early. Due to this, technology startups plateau off as niche.

Platform startups, on the other hand, struggle with retaining relevance. Due to these aspects, failure rates are very high.

This course addresses students who want to become entrepreneurs or want to join startups. They may come from business or science & technology backgrounds. The course will enable the students to identify the relevance of seeing the technology from an early stage startup from the market relevance perspective and use this to help the company drive revenue and relevance. The students will also get an overview of how platform startups can retain relevance. The students will have exposure to investors and entrepreneurs (with a focus on ETH spin-offs) through the course, to gain insight to commercialisation and subsequent scaling up of the technology.

Topics cover idea validation, technology and market size validation and assessment of market relevance, assessing time-to-market, customer focus, perceived value for customers, and finally, opportunities of maximising relevance of technology idea into sustained market traction. There is a particular emphasis on market validation on each step of the journey, to ensure relevance.

The course comprises lectures and talks from invited investors / entrepreneurs regarding the aforementioned elements. Additionally, students will form teams and will support an existing startup over the course of the semester. This will allow them to gain first-hand experience and insights into the dynamics of a early stage company. By having such real-life exposure, the course content will be transferred from theory to practice.

Grading of the course will be based on in-class presentations as well as the student teams' performance and support of their selected startups.
Literature“From Science to Startup” by A. Sethi
376-0210-00LBiomechatronics
Primarily designed for HSTstudents

The Biomechatronics lecture is not appropriate for students who already attended the lecture "Physical Human-Robot Interaction"(376-1504-00L), because it covers similar topics.

Matlab skills are beneficial-> online Tutorial Link
W4 credits3GR. Riener, R. Gassert
AbstractDevelopment of mechatronic systems (i.e. mechanics, electronics, computer science and system integration) with inspiration from biology and application in the living (human) organism.
ObjectiveThe objective of this course is to give an introduction to the fundamentals of biomechatronics, through lectures on the underlying theoretical/mechatronics aspects and application fields. In the exercises, these concepts will be intensified and trained on the basis of specific examples. The course will guide students through the design and evaluation process of such systems, and highlight a number of applications.

By the end of this course, you should understand the critical elements of biomechatronics and their interaction with biological systems, both in terms of engineering metrics and human factors. You will be able to apply the learned methods and principles to the design, improvement and evaluation of safe and efficient biomechatronics systems.
ContentThe course will cover the interdisciplinary elements of biomechatronics, ranging from human factors to sensor and actuator technologies, real-time signal processing, system kinematics and dynamics, modeling and simulation, controls and graphical rendering as well as safety/ethical aspects, and provide an overview of the diverse applications of biomechatronics technology.
Lecture notesSlides will be distributed through moodle before the lectures.
LiteratureBrooker, G. (2012). Introduction to Biomechatronics. SciTech Publishing.
Riener, R., Harders, M. (2012) Virtual Reality in Medicine. Springer, London.
Prerequisites / NoticeNone
401-0302-10LComplex Analysis Restricted registration - show details
as of 4 March 2020: The lecturer and many students are in the lecture hall, but some students are absent. The lecture is recorded.
W4 credits3V + 1UA. Iozzi
AbstractBasics of complex analysis in theory and applications, in particular the global properties of analytic functions. Introduction to the integral transforms and description of some applications
ObjectiveErwerb von einigen grundlegenden Werkzeuge der komplexen Analysis.
ContentExamples of analytic functions, Cauchy‘s theorem, Taylor and Laurent series, singularities of analytic functions, residues. Fourier series and Fourier integral, Laplace transform.
LiteratureJ. Brown, R. Churchill: "Complex Analysis and Applications", McGraw-Hill 1995

T. Needham. Visual complex analysis. Clarendon Press, Oxford. 2004.

M. Ablowitz, A. Fokas: "Complex variables: introduction and applications", Cambridge Text in Applied Mathematics, Cambridge University Press 1997

E. Kreyszig: "Advanced Engineering Analysis", Wiley 1999

J. Marsden, M. Hoffman: "Basic complex analysis", W. H. Freeman 1999

P. P. G. Dyke: "An Introduction to Laplace Transforms and Fourier Series", Springer 2004

A. Oppenheim, A. Willsky: "Signals & Systems", Prentice Hall 1997

M. Spiegel: "Laplace Transforms", Schaum's Outlines, Mc Graw Hill
Prerequisites / NoticePrerequisites: Analysis I and II
402-0810-00LComputational Quantum Physics
Special Students UZH must book the module PHY522 directly at UZH.
W8 credits2V + 2UT. Neupert, M. H. Fischer
AbstractThis course provides an introduction to simulation methods for quantum systems, starting with the one-body problem and finishing with quantum field theory, with special emphasis on quantum many-body systems. Both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, quantum Monte Carlo) are covered.
ObjectiveThe goal is to become familiar with computer simulation techniques for quantum physics, through lectures and practical programming exercises.
402-0812-00LComputational Statistical Physics Information W8 credits2V + 2UO. Zilberberg
AbstractComputer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems.

Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
ObjectiveThe lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture
"Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems.

In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements.
ContentComputer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
Lecture notesLecture notes and slides are available online and will be distributed if desired.
LiteratureLiterature recommendations and references are included in the lecture notes.
Prerequisites / NoticeSome basic knowledge about statistical physics, classical mechanics and computational methods is recommended.
402-1782-00LPhysics II
Accompanying the lecture course "Physics II", among GESS Science in Perspective is offered: 851-0147-01L Philosophical Reflections on Physics II
W7 credits4V + 2UR. Wallny
AbstractIntroduction to theory of waves, electricity and magnetism. This is the continuation of Physics I which introduced the fundamentals of mechanics.
Objectivebasic knowledge of mechanics and electricity and magnetism as well as the capability to solve physics problems related to these subjects.
636-0702-00LStatistical Models in Computational BiologyW6 credits2V + 1U + 2AN. Beerenwinkel
AbstractThe course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods.
ObjectiveThe goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets.
ContentGraphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises.
Lecture notesno
Literature- Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252
- Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007.
- Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004
  •  Page  1  of  1