Search result: Catalogue data in Spring Semester 2020
|Electrical Engineering and Information Technology Bachelor|
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).
|» Additional third year core courses may be credited as electives.|
|227-0123-00L||Mechatronics||W||6 credits||4G||T. M. Gempp|
|Abstract||Introduction 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.|
|Objective||Introduction into the basics and technology of mechatronical devices. Theoretical and practical know-how of the basic elements of a mechatronical system.|
|Content||Introduction 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 notes||Recommendation of textbook. Additional documentation to the individual topics. Documentation from industrial companies.|
|227-0216-00L||Control Systems II||W||6 credits||4G||R. Smith|
|Abstract||Introduction to basic and advanced concepts of modern feedback control.|
|Objective||Introduction to basic and advanced concepts of modern feedback control.|
|Content||This 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 notes||The slides of the lecture are available to download.|
|Literature||Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.|
|Prerequisites / Notice||Prerequisites:|
Control Systems or equivalent
|376-0022-00L||Imaging and Computing in Medicine||W||4 credits||3G||R. Müller, P. Christen, C. J. Collins|
|Abstract||Imaging 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.|
|Objective||1. 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
|Content||Imaging 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 notes||Stored on Moodle.|
|Prerequisites / Notice||Lectures will be given in English.|
|252-0834-00L||Information Systems for Engineers |
Wird ab HS20 nur in Herbstsemester angeboten.
|W||4 credits||2V + 1U||G. Fourny|
|Abstract||This 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).
|Objective||This 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.
|Content||Using a relational database|
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
|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 / Notice||For 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
|252-0220-00L||Introduction to Machine Learning |
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 firstname.lastname@example.org
|W||8 credits||4V + 2U + 1A||A. Krause|
|Abstract||The course introduces the foundations of learning and making predictions based on data.|
|Objective||The 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)
|Literature||Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press|
|Prerequisites / Notice||Designed to provide a basis for following courses:|
- Advanced Machine Learning
- Deep Learning
- Probabilistic Artificial Intelligence
- Seminar "Advanced Topics in Machine Learning"
|252-3800-00L||Advanced Topics in Technical Human-Computer Interaction |
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.
|W||2 credits||2S||C. Holz|
|Abstract||We 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.|
|Objective||The 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.|
|Content||The 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.|
|Literature||24 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 / Notice||All 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.|
|227-0669-00L||Chemistry of Devices and Technologies |
Limited to 30 participants.
|W||4 credits||1V + 2U||M. Yarema|
|Abstract||The 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.|
|Objective||The 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.
|Content||Students 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.
|Literature||Lecture notes will be made available on the website.|
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