Search result: Catalogue data in Spring Semester 2022
Doctorate Computer Science  More Information at: https://www.ethz.ch/en/doctorate.html | ||||||
 Subject Specialisation | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 151-0906-00L | Frontiers in Energy Research Does not take place this semester. This course is only for doctoral students. | W | 2 credits | 2S | ||
| Abstract | Doctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, their advisors and the scientific community. Each week a different student gives a 50-60 min presentation of their research (a full introduction, background & findings) followed by discussion with the audience. | |||||
| Learning objective | The key objectives of the course are: (1) participants will gain knowledge of advanced research in the area of energy; (2) participants will actively participate in discussion after each presentation; (3) participants gain experience of different presentation styles; (4) to create a network amongst the energy research doctoral student community. | |||||
| Content | Doctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, to their advisors and to the scientific community. There will be one presentation a week during the semester, each structured as follows: 20 min introduction to the research topic, 30 min presentation of the results, 30 min discussion with the audience. | |||||
| Lecture notes | Slides will be available on the Energy Science Center pages(www.esc.ethz.ch/events/frontiers-in-energy-research.html). | |||||
| 252-0945-14L | Doctoral Seminar Machine Learning (FS22) Only for Computer Science Ph.D. students. This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar. | W | 2 credits | 1S | N. He, M. Sachan, A. Krause, G. Rätsch | |
| Abstract | An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills. | |||||
| Learning objective | The seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills. | |||||
| Prerequisites / Notice | This doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab. | |||||
| 252-4202-00L | Seminar in Theoretical Computer Science | W | 2 credits | 2S | E. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, D. Steurer, B. Sudakov | |
| Abstract | Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates. | |||||
| Learning objective | To get an overview of current research in the areas covered by the involved research groups. To present results from the literature. | |||||
| Prerequisites / Notice | This seminar takes place as part of the joint research seminar of several theory groups. Intended participation is for students with excellent performance only. Formal restriction is: prior successful participation in a master level seminar in theoretical computer science. | |||||
| 263-2100-00L | Research Topics in Software Engineering  Number of participants limited to 22. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | Z. Su, M. Vechev | |
| Abstract | This seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research. | |||||
| Learning objective | Each student will be asked to study some papers from the recent software engineering literature and review them. This is an exercise in critical review and analysis. Active participation is required (a presentation of a paper as well as participation in discussions). | |||||
| Content | The aim of this seminar is to introduce students to recent research results in the area of programming languages and software engineering. To accomplish that, students will study and present research papers in the area as well as participate in paper discussions. The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools. | |||||
| Literature | The publications to be presented will be announced on the seminar home page at least one week before the first session. | |||||
| Prerequisites / Notice | Papers will be distributed during the first lecture. | |||||
| 263-4203-00L | Geometry: Combinatorics and Algorithms 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 | B. Gärtner, M. Hoffmann, E. Welzl, J. Cardinal, M. Wettstein | |
| Abstract | This seminar complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. | |||||
| Learning objective | Each student is expected to read, understand, and elaborate on a selected research paper. To this end, (s)he should give a 45-min. presentation about the paper. The process includes * getting an overview of the related literature; * understanding and working out the background/motivation: why and where are the questions addressed relevant? * understanding the contents of the paper in all details; * selecting parts suitable for the presentation; * presenting the selected parts in such a way that an audience with some basic background in geometry and graph theory can easily understand and appreciate it. | |||||
| Content | This seminar is held once a year and complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. The seminar is a good preparation for a master, diploma, or semester thesis in the area. | |||||
| Prerequisites / Notice | Prerequisite: Successful participation in the course "Geometry: Combinatorics & Algorithms" (takes place every HS) is required. | |||||
| 263-4660-00L | Applied Cryptography Number of participants limited to 150. | W | 8 credits | 3V + 2U + 2P | K. Paterson | |
| Abstract | This course will introduce the basic primitives of cryptography, using rigorous syntax and game-based security definitions. The course will show how these primitives can be combined to build cryptographic protocols and systems. | |||||
| Learning objective | The goal of the course is to put students' understanding of cryptography on sound foundations, to enable them to start to build well-designed cryptographic systems, and to expose them to some of the pitfalls that arise when doing so. | |||||
| Content | Basic symmetric primitives (block ciphers, modes, hash functions); generic composition; AEAD; basic secure channels; basic public key primitives (encryption,signature, DH key exchange); ECC; randomness; applications. | |||||
| Literature | Textbook: Boneh and Shoup, “A Graduate Course in Applied Cryptography”, https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf. | |||||
| Prerequisites / Notice | Students should have taken the D-INFK Bachelor's course “Information Security" (252-0211-00) or an alternative first course covering cryptography at a similar level. / In this course, we will use Moodle for content delivery: https://moodle-app2.let.ethz.ch/course/view.php?id=14558. | |||||
| 263-5051-00L | AI Center Projects in Machine Learning Research Number of participants limited to 50. Last cancellation/deregistration date for this ungraded semester performance: Friday, 18 March 2022! Please note that after that date no deregistration will be accepted and the course will be considered as "fail". | W | 4 credits | 2V + 1A | A. Ilic, M. El-Assady, F. Engelmann, T. Kontogianni, A. Marx, G. Ramponi, A. Sanyal, M. Sorbaro Sindaci | |
| Abstract | The course will give students an overview of selected topics in advanced machine learning that are currently subjects of active research. The course concludes with a final project. | |||||
| Learning objective | The overall objective is to give students a concrete idea of what working in contemporary machine learning research is like and inform them about current research performed at ETH. In this course, students will be able to get an overview of current research topics in different specialized areas. Each topic is accompanied by small hands-on exercises that prepare for the final project. In the final project, students will be able to build experience in practical aspects of machine learning research, including research literature, aspects of implementation, and reproducibility challenges. | |||||
| Content | The course will be structured as sections taught by different PostDocs specialized in the relevant fields. Each section will showcase an advanced research topic in machine learning, first introducing it and motivating it in the context of current technological or scientific advancement, then providing practical applications that students can experiment with, ideally with the aim of reproducing a very simple, known result in the specific field. The tentative list of topics for this year is 3D scene understanding, graph neural networks, causal discovery, event-based sensors, trustworthy AI, reinforcement learning and visual text analytics. The last weeks of the course will be reserved for the implementation of the final project that the students can select among one of the presented areas. | |||||
| Prerequisites / Notice | Participants should have basic knowledge about machine learning and statistics (e.g. Introduction to Machine Learning course or equivalent) and programming. | |||||
| 263-5300-00L | Guarantees for Machine Learning Does not take place this semester. Number of participants limited to 30. The course will take place next autumn semester 2022. | W | 7 credits | 3G + 3A | F. Yang | |
| Abstract | This course is aimed at advanced master and doctorate students who want to conduct independent research on theory for modern machine learning (ML). It teaches classical and recent methods in statistical learning theory commonly used to prove theoretical guarantees for ML algorithms. The knowledge is then applied in independent project work that focuses on understanding modern ML phenomena. | |||||
| Learning objective | Learning objectives: - acquire enough mathematical background to understand a good fraction of theory papers published in the typical ML venues. For this purpose, students will learn common mathematical techniques from statistics and optimization in the first part of the course and apply this knowledge in the project work - critically examine recently published work in terms of relevance and determine impactful (novel) research problems. This will be an integral part of the project work and involves experimental as well as theoretical questions - find and outline an approach (some subproblem) to prove a conjectured theorem. This will be practiced in lectures / exercise and homeworks and potentially in the final project. - effectively communicate and present the problem motivation, new insights and results to a technical audience. This will be primarily learned via the final presentation and report as well as during peer-grading of peer talks. | |||||
| Content | This course touches upon foundational methods in statistical learning theory aimed at proving theoretical guarantees for machine learning algorithms, touching on the following topics - concentration bounds - uniform convergence and empirical process theory - high-dimensional statistics (e.g. sparsity) - regularization for non-parametric statistics (e.g. in RKHS, neural networks) - implicit regularization via gradient descent (e.g. margins, early stopping) - minimax lower bounds The project work focuses on current theoretical ML research that aims to understand modern phenomena in machine learning, including but not limited to - how overparameterization could help generalization ( RKHS, NN ) - how overparameterization could help optimization ( non-convex optimization, loss landscape ) - complexity measures and approximation theoretic properties of randomly initialized and trained NN - generalization of robust learning ( adversarial robustness, standard and robust error tradeoff, distribution shift) | |||||
| Prerequisites / Notice | It’s absolutely necessary for students to have a strong mathematical background (basic real analysis, probability theory, linear algebra) and good knowledge of core concepts in machine learning taught in courses such as “Introduction to Machine Learning”, “Regression”/ “Statistical Modelling”. In addition to these prerequisites, this class requires a high degree of mathematical maturity—including abstract thinking and the ability to understand and write proofs. Students have usually taken a subset of Fundamentals of Mathematical Statistics, Probabilistic AI, Neural Network Theory, Optimization for Data Science, Advanced ML, Statistical Learning Theory, Probability Theory (D-MATH) | |||||
| 264-5800-19L | Doctoral Seminar in Visual Computing (FS22) | W | 1 credit | 1S | M. Gross, M. Pollefeys, O. Sorkine Hornung, S. Tang | |
| Abstract | In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges caThis graduate seminar provides doctoral students in computer science a chance to read and discuss current research papers. | |||||
| Learning objective | In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges can be discussed, and also to practice scientific presentations. | |||||
| Content | Current research at the IVC will be presented and discussed. | |||||
| Prerequisites / Notice | This course requires solid knowledge in the area of Computer Graphics and Computer Vision as well as state-of-the-art research. | |||||
| 264-5812-00L | Writing for Publication in Computer Science A (WPCS) Only for D-INFK doctoral students. Number of participants limited to 15. | Z | 2 credits | 1G | S. Milligan | |
| Abstract | This short course is designed to help junior researchers in Computer Science develop the skills needed to write their first research articles. | |||||
| Learning objective | Writing for Publication in Computer Science is a short course (5 x 4-lesson workshops) designed to help doctoral students develop the skills needed to write their first research articles. The course deals with topics such as: - understanding the needs of different target readerships, - managing the writing process efficiently, - structuring texts effectively, - producing logical flow in sentences and paragraphs, - editing texts before submission, and - revising texts in response to colleagues' feedback and reviewers' comments. | |||||
| Content | Participants will be expected to produce a number of short texts (e.g., draft of a conference abstract) as homework assignments; they will receive individual feedback on these texts during the course. Wherever feasible, elements of participants' future conference/journal articles can be developed as assignments within the course, so it is likely to be particularly useful for those who have i) their data and are about to begin the writing process, or ii) an MSc thesis they would like to convert for publication. | |||||
| 264-5813-00L | Writing for Publication in Computer Science B (WPCS) Only for D-INFK doctoral students. Number of participants limited to 15. | Z | 2 credits | 1G | S. Milligan | |
| Abstract | This short course is designed to help junior researchers in Computer Science develop the skills needed to write their first research articles. | |||||
| Learning objective | Writing for Publication in Computer Science is a short course (5 x 4-lesson workshops) designed to help doctoral students develop the skills needed to write their first research articles. The course deals with topics such as: - understanding the needs of different target readerships, - managing the writing process efficiently, - structuring texts effectively, - producing logical flow in sentences and paragraphs, - editing texts before submission, and - revising texts in response to colleagues' feedback and reviewers' comments. | |||||
| Content | Participants will be expected to produce a number of short texts (e.g., draft of a conference abstract) as homework assignments; they will receive individual feedback on these texts during the course. Wherever feasible, elements of participants' future conference/journal articles can be developed as assignments within the course, so it is likely to be particularly useful for those who have i) their data and are about to begin the writing process, or ii) an MSc thesis they would like to convert for publication. | |||||
| 327-2225-00L | MaP Distinguished Lecture Series on Soft Robotics Does not take place this semester. This course is primarily designed for MSc and doctoral students. Guests are welcome. | W | 1 credit | 2S | R. Katzschmann | |
| Abstract | This course is an interdisciplinary colloquium on Soft Robotics involving different internationally renowned speakers from academia and industry giving lectures about their cutting-edge research, which highlights the state-of-the-art and frontiers in the Soft Robotics field. | |||||
| Learning objective | Participants become acquainted with the state-of-the-art and frontiers in Soft Robotics, which is a topic of global and future relevance from the field of materials and process engineering. The self-study of relevant literature and active participation in discussions following presentations by internationally renowned speakers stimulate critical thinking and allow participants to deliberately discuss challenges and opportunities with leading academics and industrial experts and to exchange ideas within an interdisciplinary community. | |||||
| Content | This course is a colloquium involving a selected mix of internationally renowned speaker from academia and industry who present their cutting-edge research in the field of Soft Robotics. The self-study of relevant pre-read literature provided in advance to each lecture serves as a basis for active participation in the critical discussions following each presentation. | |||||
| Lecture notes | Selected scientific pre-read literature (max. three articles per lecture) relevant for and discussed during the lectures is posted in advance on the course web page. | |||||
| Prerequisites / Notice | Participants should have a solid background in materials science and/or engineering. | |||||
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