Search result: Catalogue data in Spring Semester 2021

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 (Link).
Engineering Electives
NumberTitleTypeECTSHoursLecturers
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
227-0518-10LDesign and Control of Electric MachinesW6 credits4GD. Bortis
AbstractThis course covers modeling and control concepts of modern drive systems and provides a deeper understanding of the dynamic operation of electric machines. Different aspects arising in the design of electric drive systems are investigated. The exercises are used to consolidate the concepts discussed.
ObjectiveThe objective of this course is to convey knowledge on control strategies of different types of electric machines and on design principles of variable speed drive systems. A dynamic modeling of the electromechanical system is investigated, enabling the proper design of cascaded speed, torque/current controllers. Further objectives are the identification of machine parameters and a short insight into basic inverter circuits applied in advanced motor drive systems. Exercises are used to consolidate the presented theoretical concepts.
Content1. Introduction to variable speed motor drive systems consisting of:
- Electromechanical system
- Power electronic system
- Control system
- Measurement system

2. Control structures and strategies of DC Machine/Synchronous machine/Asynchronous machine/Brushless DC machine.
- Cascaded control
- U/f Control
- Slip Control
- Field-oriented control

3. Dynamic Operation of electric machines
- Dynamic modeling of electromechanical system
- Controller types and design
- Current/torque control
- Speed control (Voltage control / Flux weakening)

4. Power electronic inverter circuits in variable speed drive systems
- Voltage and current source inverter systems
- Basic operation and pulse width modulation

5. Identification of machine parameters

6. Design principles of variable speed motor drives systems
Lecture notesLecture notes and associated exercises including correct answers
Prerequisites / NoticePrerequisites: Fundamentals of Electric Machines
376-0022-00LImaging and Computing in Medicine Information Restricted registration - show details W4 credits3GR. Müller, C. J. Collins
AbstractImaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamentals 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 fundamentals 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 are able to post a number of questions in the Moodle forum or directly in the comments section of the video lecture that will be addressed in the second part of the lectures using a flipped classroom concept. For the flipped classroom, the lecturers may prepare additional teaching material to answer the posted questions and potentially discuss further questions (Q&A). Following the Q&A, the students will form small groups to acquire additional knowledge using online, interactive activities or additionally distributed material and discuss their findings in teams. Learning outcomes will be reinforced with weekly Moodle assignments, to be completed during the flipped classroom portion.
Lecture notesStored on Moodle.
Prerequisites / NoticeLectures will be given in English.
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 Link
W8 credits4V + 2U + 1AA. Krause, F. Yang
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 Human-Computer Interaction and Computational 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.
227-2211-00LSeminar in Computer Architecture Information Restricted registration - show details
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.
W2 credits2SO. Mutlu, M. H. K. Alser, J. Gómez Luna
AbstractThis seminar course covers fundamental and cutting-edge research papers in computer architecture. It has multiple components that are aimed at improving students' (1) technical skills in computer architecture, (2) critical thinking and analysis abilities on computer architecture concepts, as well as (3) technical presentation of concepts and papers in both spoken and written forms.
ObjectiveThe main objective is to learn how to rigorously analyze and present papers and ideas on 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 meeting, participate in the discussion, and create a synthesis report at the end of the course.
ContentTopics will center around computer architecture. We will, for example, discuss papers on hardware security; accelerators for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; processing in 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; predictable computing, etc.
Lecture notesAll materials will be posted on the course website: Link
Past course materials, including the synthesis report assignment, can be found in the Fall 2020 website for the course: Link
LiteratureKey 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 / NoticeDesign of Digital Circuits.
Students should (1) have done very well in Design of Digital Circuits and (2) show a genuine interest in Computer Architecture.
Man-Technology-Environment Electives ("MTU")
NumberTitleTypeECTSHoursLecturers
227-0803-00LEnergy, Resources, Environment: Risks and ProspectsW6 credits4GO. Zenklusen, T. Flüeler
AbstractMultidisciplinary, interactive course focusing on the complexity of environmental and energy problems. Concepts of risk theory, decision science, long-term governance and environmental economics are applied to case studies related to energy transition and climate change. The course is designed for a multidisciplinary audience and as a training ground for critical thinking.
ObjectiveDevelop capacities for addressing environmental problems, scrutinising proposed solutions and contributing to debates across disciplines. Analyse complex issues from different perspectives and using a variety of concepts. Understand interactions between the environment, science and technology, society and economy. Develop skills in critical thinking, scientific writing and presenting.
ContentFollowing a multidisciplinary outline of current issues in environmental and energy policy as well as the concept of "messy problems”, the course introduces theoretical and analytical approaches including risk, sustainability, as well as elements of 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 as starting points for debates. Topics include: energy transition, innovation, the potential of renewable energy, carbon markets, the future of nuclear energy, climate change and development policy, long-term issues in various fields, 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
Prerequisites / NoticeThe elcture is held online (ZOOM) till end of April. Then we will evalute the situation.
GESS Science in Perspective
Science in Perspective
» see Science in Perspective: Type A: Enhancement of Reflection Capability
» Recommended Science in Perspective (Type B) for D-ITET
Language Courses
» see Science in Perspective: Language Courses ETH/UZH
Bachelor's Project
The Bachelor's Thesis is the final part of the bachelor's program and should therefore only be taken in the semester in which the bachelor's diploma is acquired.

The minimum requirement for enrollment is the successful completion of:
- basic examination (examination blocks A+B) and
- subjects of the second year (examination blocks 1-3)
NumberTitleTypeECTSHoursLecturers
227-0100-00LBachelor's Thesis Restricted registration - show details
A 14 week long Bachelor's Thesis is the final part of the bachelor's program and shall therefore be taken during the semester in which the bachelor's diploma is acquired.

The minimum requirement for enrollment is the successful completion of:
- basic examination (examination blocks A+B)
- subjects of the second year (examination blocks 1-3)

Supervisor must be a professor at D-ITET or associated, see a link to the lists of those at Link
O12 credits26DSupervisors
AbstractDuring the Bachelor's Thesis, students will gain initial experience in the independent solution of a technical-scientific problem by applying the acquired specialist and social skills.
A Bachelor's Thesis should take about half of a student's time during one semester, i.e., about 300-400 hours. The thesis includes an oral presentation and a written report, and it is graded.
Objectivesee above
Prerequisites / NoticeA 14 week long Bachelor's Thesis is the final part of the bachelor's program and shall therefore be taken during the semester in which the bachelor's diploma is acquired.

The minimum requirement for enrollment is the successful completion of:
- basic examination (examination blocks A+B)
- subjects of the second year (examination blocks 1-3)

Supervisor must be a professor at D-ITET or associated, see a link to the lists of those at Link
227-1101-00LHow to Write Scientific Texts
Strongly recommended prerequisite for Semester Projects, Bachelor's, and Master Theses at D-ITET (MSc BME, BSc/MSc EEIT, MSc EST and MSc QE).
E-0 creditsU. Koch
AbstractThe four hour lecture covers the basics of writing and presenting of scientific work. The focus is on the structure and the main elements of a scientific text rather than the language. Citation rules, good practice of scientific writing and an overview on software tools are part of the training.
Objective- Knowledge on structure and content of scientific texts and presentations
- Stimulation of a discussion on how to write a scientific text versus an interesting novel
- Discussion of the practice of proper citing and critical reflection on plagiarism
Content* Topic 1: Structure of Scientific Texts (title, author list, abstract, state-of-the-art, "in this paper" paragraph, scientific part, summary, equations, figures)

* Topic 2: Structure of Scientific Presentations

* Topic 3: Citation Rules and Citation Software

* Topic 4: Guidelines for Research Integrity

The lecture will be given in two parts on two afternoons. Some exercises will be built into the lecture.
LiteratureETH "Citation Etiquette", see Link.

ETH "Guidelines for Research Integrity", see Link
Prerequisites / NoticeStudents should be writing either a bachelor/semester/master thesis or a scientific publication in the immediate future.
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