Search result: Catalogue data in Spring Semester 2020

Electrical Engineering and Information Technology Bachelor Information
Electives
This is only a short selection. Other courses from the ETH course catalogue may be chosen. Please consult the "Richtlinien zu Projekten, Praktika, Seminare" (German only), published on our website (http://www.ee.ethz.ch/pps-richtlinien).
Economics, Law and Management Electives
These subjects are particularly suitable for students planning to apply to the Master's Degree Program in Energy Science and Technology (MSc EST) or Management, Technology and Economics (MSc MTEC).
NumberTitleTypeECTSHoursLecturers
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.
Engineering Electives
NumberTitleTypeECTSHoursLecturers
» Additional third year core courses may be credited as electives.
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.
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
Lecture notesThe slides of the lecture are available to download.
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
376-0022-00LImaging and Computing in Medicine Information Restricted registration - show details W4 credits3GR. Müller, P. Christen, C. J. Collins
AbstractImaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamental as well as modern techniques and methods of imaging and computing in medicine.
Objective1. Understanding and practical implementation of biosignal processes methods for imaging
2. Understanding of imaging techniques including radiation imaging, radiographic imaging systems, computed tomography imaging, diagnostic ultrasound imaging, and magnetic resonance imaging
3. Knowledge of computing, programming, modelling and simulation fundamentals
4. Computational and systems thinking as well as scripting and programming skills
5. Understanding and practical implementation of emerging computational methods and their application in medicine including artificial intelligence, deep learning, big data, and complexity
6. Understanding of the emerging concept of personalised and in silico medicine
7. Encouragement of critical thinking and creating an environment for independent and self-directed studying
ContentImaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamental as well as modern techniques and methods of imaging and computing in medicine. For the imaging portion of the course, biosignal processing, radiation imaging, radiographic imaging systems, computed tomography imaging, diagnostic ultrasound imaging, and magnetic resonance imaging are covered. For the computing portion of the course, computing, programming, and modelling and simulation fundamentals are covered as well as their application in artificial intelligence and deep learning; complexity and systems medicine; big data and personalised medicine; and computational physiology and in silico medicine.
The course is structured as a seminar in three parts of 45 minutes with video lectures and a flipped classroom setup: in the first part (TORQUEs: Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness), students study the basic concepts in short video lectures on the online learning platform Moodle. At the end of this first part, students must post a number of questions in the Moodle forum that will be addressed in the second part of the lectures using a flipped classroom concept. First, the lecturers may prepare additional teaching material to answer the posted questions and potentially discuss further questions (Q&A). Second, the students will form small groups to acquire additional knowledge online or from additionally distributed material and to present their findings to the rest of the class.
Lecture notesStored on Moodle.
Prerequisites / NoticeLectures will be given in English.
252-0834-00LInformation Systems for Engineers Information
Wird ab HS20 nur in Herbstsemester angeboten.
W4 credits2V + 1UG. Fourny
AbstractThis course provides the basics of relational databases from the perspective of the user.

We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics).
ObjectiveThis lesson is complementary with Big Data for Engineers as they cover different time periods of database history and practices -- you can even take both lectures at the same time.

After visiting this course, you will be capable to:

1. Explain, in the big picture, how a relational database works and what it can do in your own words.

2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc).

3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data.

4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality

5. Explain what bad design is and why it matters.

6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms".

7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster.

8. Access an existing relational database from a host language such as Java, using bridges such as JDBC.

9. Explain what data independence is all about and didn't age a bit since the 1970s.

10. Explain, in the big picture, how a relational database is physically implemented.

11. Know and deal with the natural syntax for relational data, CSV.

12. Explain the data cube model including slicing and dicing.

13. Store data cubes in a relational database.

14. Map cube queries to SQL.

15. Slice and dice cubes in a UI.

And of course, you will think that tables are the most wonderful object in the world.
ContentUsing a relational database
=================
1. Introduction
2. The relational model
3. Data definition with SQL
4. The relational algebra
5. Queries with SQL

Taking a relational database to the next level
=================
6. Database design theory
7. Databases and host languages
8. Databases and host languages
9. Indices and optimization
10. Database architecture and storage

Analytics on top of a relational database
=================
12. Data cubes

Outlook
=================
13. Outlook
Literature- Lecture material (slides).

- Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom
(It is not required to buy the book, as the library has it)
Prerequisites / NoticeFor non-CS/DS students only, BSc and MSc
Elementary knowledge of set theory and logics
Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python
252-0220-00LIntroduction to Machine Learning Information Restricted registration - show details
Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch
W8 credits4V + 2U + 1AA. Krause
AbstractThe course introduces the foundations of learning and making predictions based on data.
ObjectiveThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Content- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor
- Neural networks (backpropagation, regularization, convolutional neural networks)
- Unsupervised learning (k-means, PCA, neural network autoencoders)
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
LiteratureTextbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Prerequisites / NoticeDesigned to provide a basis for following courses:
- Advanced Machine Learning
- Deep Learning
- Probabilistic Artificial Intelligence
- Seminar "Advanced Topics in Machine Learning"
252-3800-00LAdvanced Topics in Technical Human-Computer Interaction Information Restricted registration - show details
Number of participants limited to 24.

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.
W2 credits2SC. Holz
AbstractWe will discuss the latest topics in HCI and related communities: interactive devices, wearable and mobile sensing, applied computer vision for gesture, hand, and body pose input, machine learning-based processing. assistive and accessible technologies, biometrics & authentication, fabrication, haptic feedback, Augmented Reality, Virtual Reality, projection-based systems, affective computing.
ObjectiveThe objective of the seminar is for participants to collectively learn about the state-of-the-art research in Human-Computer Interaction and closely related areas. Another objective is to collectively discuss open issues in the field, necessary follow-up work for the latest presented results in the field, and developing a feeling for what constitutes research questions and outcomes in the field of technical Human-Computer Interaction.
ContentThe seminar format is as follows: attendees individually read one recent full-paper publication, working through its content in detail and possibly covering some of the background if necessary, and present the approach, methodology, research question and implementation as well as the evaluation and discussion in a 20–25 min talk in front of the others. Each presenter will then lead a short discussion about the paper, which is guided by questions posed to the audience in advance.
Literature24 papers will be provided by the lecturer and distributed in the first seminar on a first-come, first-served basis according to participants' preferences. The lecturer will also give a brief run-down across all 24 papers in a fast-forward style, covering each paper in a single-minute presentation, and outline the difficulties of each project. The schedule is fixed throughout the term with easier papers being presented earlier and more comprehensive papers presented later in the term.
Prerequisites / NoticeAll students are welcome in the first seminar to see the overview over the papers we will discuss. After assigning papers, the seminar will be limited to 24 attendees, i.e., those students that sign up for papers first.
227-0669-00LChemistry of Devices and Technologies Restricted registration - show details
Limited to 30 participants.
W4 credits1V + 2UM. Yarema
AbstractThe course covers basics of chemistry and material science, relevant for modern devices and technologies. The course consists from lecture, laboratory, and individual components. Students accomplish individual projects, in which they study and evaluate a chosen technology from chemistry and materials viewpoints.
ObjectiveThe course brings relevant chemistry knowledge, tailored to the needs of electrical engineering students. Students will gain understanding of the basic concepts of chemistry and a chemist's intuition through hands-on workshops that combine tutorials and laboratory sessions as well as guidance through individual projects that require interdisciplinary and critical thinking.
Students will learn which materials, reactions, and device fabrication processes are important for nowadays technologies and products. They will gain important knowledge of state-of-the-art technologies from materials and fabrication viewpoints.
ContentStudents will spend 3h per week in the tutorials and practical sessions and additional 4-6h per week working on individual projects.
The goal of the individual student's project is to understand the chemistry related to the manufacture and operation of a specific device or technology (to be chosen from the list of projects). To ensure continued learning throughout the semester, individual projects are evaluated by three interim project reports and by 10 min final presentation.
LiteratureLecture notes will be made available on the website.
Man-Technology-Environment Electives ("MTU")
NumberTitleTypeECTSHoursLecturers
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-
151-0228-00LManagement of Air Transport (Aviation II)W4 credits3GP. Wild
AbstractProviding an overview in management, planning, processes and operations in air transport, the lecture shall enable students to operate and lead a unit within that industry. In addition, the modules provide a good understanding for other transport modes and are a sort of "Mini MBA" (topics see below). Ideally, students complete first "Basics in Air Transport" yet there is no requirement for it.
ObjectiveAfter completion of the course, they shall be familiar with tasks, processes and interactions and have the ability to understand implications of developments in the airlines industry and its environment. This shall enable them to work within the air transport industry.
ContentWeekly: 1h independent preparation; 2h lectures and 1 h training with an expert in the respective field
Overall concept: This lecture build on the content of the lecture "Basics in Air Transport" (101-0499-00L) and provides deeper insights into the airline industry.
Content: Strategy, Alliances & Joint Ventures, Negotiations with Stakeholder, Environmental Protection, Safety & Risk Management, Airline Economics, Network Management, Revenue Management & Pricing, Sales & Distribution, Airline Marketing, Scheduling & Slot Management, Fleet Management & Leasing, Continuing Airworthiness Management, Supply Chain Management, Operational Steering
Lecture notesNo offical lecture notes. Lecturers' slides will be made available
LiteratureLiterature will be provided by the lecturers respective there will be additional Information upon registration
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