Search result: Catalogue data in Autumn Semester 2022
Computer Science Master  | |||||||||||||||
 Seminar | |||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 252-3811-00L | Case Studies from Practice Seminar  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 | 4 credits | 2S | M. Brandis | ||||||||||
| Abstract | Participants will learn how to analyze and solve IT problems in practice in a systematic way, present findings to decision bodies, and defend their conclusions. | ||||||||||||||
| Learning objective | Participants understand the different viewpoints for IT-decisions in practice, including technical and business aspects, can effectively analyze IT questions from the different viewpoints and facilitate decision making. | ||||||||||||||
| Content | Participants learn how to systematically approach an IT problem in practice. They work in groups of three to solve a case from a participating company in depth, studying provided materials, searching for additional information, analyzing all in depth, interviewing members from the company or discussing findings with them to obtain further insights, and presenting and defending their conclusion to company representatives, the lecturer, and all other participants of the seminar. Participants also learn how to challenge presentations from other teams, and obtain an overview of learnings from the cases other teams worked on. | ||||||||||||||
| Lecture notes | Methodologies to analyze the cases and create final presentations. Short overview of each case. | ||||||||||||||
| Prerequisites / Notice | Successful completion of Lecture "Case Studies from Practice". | ||||||||||||||
| 252-4601-00L | Current Topics in Information Security 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 | S. Capkun, K. Paterson, A. Perrig, S. Shinde | ||||||||||
| Abstract | The seminar covers various topics in information security: security protocols (models, specification & verification), trust management, access control, non-interference, side-channel attacks, identity-based cryptography, host-based attack detection, anomaly detection in backbone networks, key-management for sensor networks. | ||||||||||||||
| Learning objective | The main goals of the seminar are the independent study of scientific literature and assessment of its contributions as well as learning and practicing presentation techniques. | ||||||||||||||
| Content | The seminar covers various topics in information security, including network security, cryptography and security protocols. The participants are expected to read a scientific paper and present it in a 35-40 min talk. At the beginning of the semester a short introduction to presentation techniques will be given. Selected Topics - security protocols: models, specification & verification - trust management, access control and non-interference - side-channel attacks - identity-based cryptography - host-based attack detection - anomaly detection in backbone networks - key-management for sensor networks | ||||||||||||||
| Literature | The reading list will be published on the course web site. | ||||||||||||||
| 252-5051-00L | Advanced Topics in Machine Learning Number of participants limited to 40. The deadline for deregistering expires at the end of the fourth 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 | J. M. Buhmann, R. Cotterell, N. He, F. Yang, M. El-Assady | ||||||||||
| Abstract | In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning. | ||||||||||||||
| Learning objective | The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations. | ||||||||||||||
| Content | The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models. | ||||||||||||||
| Literature | The papers will be presented in the first session of the seminar. | ||||||||||||||
| 252-5701-00L | Seminar in Advanced Topics in Vision Number of participants limited to 24. The deadline for deregistering expires at the end of the third 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 | M. Pollefeys, S. Tang | ||||||||||
| Abstract | This seminar covers advanced topics in computer vision, such as 3D reconstruction, image understanding, object detection, people tracking, motion prediction, and other related topics. Each time the course is offered, a collection of research papers is selected and each student presents one paper to the class and leads a discussion about the paper and related topics. | ||||||||||||||
| Learning objective | The goal is to get an in-depth understanding of actual problems and research topics in the field of computer vision as well as improve presentations and critical analysis skills. | ||||||||||||||
| Content | This seminar covers advanced topics in computer vision by reading and presenting classic and state-of-the-art papers. Each time the course is offered, a collection of research papers are selected covering topics such as 3D reconstruction, image understanding, object detection, people tracking, motion prediction and others. Each student presents one paper to the class and leads a discussion about the paper and related topics. All students read the papers and participate in the discussion. | ||||||||||||||
| Lecture notes | no script | ||||||||||||||
| Literature | Individual research papers are selected each term. | ||||||||||||||
| 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 | P. Müller, M. Püschel | ||||||||||
| 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. A particular focus will be on domain-specific languages. | ||||||||||||||
| Literature | The publications to be presented will be announced on the seminar home page at least one week before the first session. | ||||||||||||||
| Prerequisites / Notice | Organizational note: the seminar will meet only when there is a scheduled presentation. Please consult the seminar's home page for information. | ||||||||||||||
| 263-3504-00L | Hardware Acceleration for Data Processing 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 | G. Alonso | ||||||||||
| Abstract | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | ||||||||||||||
| Learning objective | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | ||||||||||||||
| Content | The general application areas are big data and machine learning. The systems covered will include systems from computer architecture, high performance computing, data appliances, and data centers. | ||||||||||||||
| Prerequisites / Notice | Students taking this seminar should have the necessary background in systems and low level programming. | ||||||||||||||
| 263-3713-00L | Advanced Topics in Human-Centric Computer Vision Numbers of participants limited to 20. The deadline for deregistering expires at the end of the third 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 | O. Hilliges | ||||||||||
| Abstract | In this seminar we will discuss state-of-the-art literature on human-centric computer vision topics including but not limited to human pose estimation, hand and eye-gaze estimation as well as generative modeliing of detailed human activities. | ||||||||||||||
| Learning objective | The learning objective is to analyze selected research papers published at top computer vision and machine learning venues. A key focus will be placed on identifying and discussing open problems and novel solutions in this space. The seminar will achieve this via several components: reading papers, technical presentations, writing analysis and critique summaries, class discussions, and exploration of potential research topics. | ||||||||||||||
| Content | The goal of the seminar is not only to familiarize students with exciting new research topics, but also to teach basic scientific writing and oral presentation skills. The seminar will have a different structure from regular seminars to encourage more discussion and a deeper learning experience. We will treat papers as case studies and discuss them in-depth in the seminar. Once per semester, every student will have to take one of the following roles: Presenter: Give a presentation about the paper that you read in depth. Reviewer: Perform a critical review of the paper. All other students: read the paper and submit questions they have about the paper before the presentation. | ||||||||||||||
| Prerequisites / Notice | Participation will be limited subject to available topics. Furthermore, students will have to submit a motivation paragraph. Participants will be selected based on this paragraph. | ||||||||||||||
| Competencies |
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| 263-4410-00L | Seminar on Advanced Graph Algorithms and Optimization Number of participants limited to 6! 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 | R. Kyng | ||||||||||
| Abstract | This seminar aims to familiarize students with current research topics in fast graph algorithms and optimization. | ||||||||||||||
| Learning objective | Read papers on cutting edge research topics; learn how to give a scientific talk. | ||||||||||||||
| Content | We will study recent papers that made significant contributions in the areas in fast graph algorithms and optimization. | ||||||||||||||
| Prerequisites / Notice | As prerequisite we require that you passed the course "Advanced Graph Algorithms and Optimization". In exceptional cases, students who passed one of the courses "Randomized Algorithms and Probabilistic Methods", "Optimization for Data Science", or "Advanced Algorithms" may also participate, at the discretion of the lecturer. | ||||||||||||||
| 263-5100-00L | Topics in Medical Machine Learning Number of participants limited to 18. The deadline for deregistering expires at the end of the fourth 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 | G. Rätsch, J. Vogt | ||||||||||
| Abstract | This seminar discusses recent relevant contributions to the fields of medical machine learning and related areas. Each participant will hold a presentation and lead the subsequent discussion. | ||||||||||||||
| Learning objective | Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to successfully present the key points of existing research work are the same as those needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class. | ||||||||||||||
| Content | The topics covered in the seminar are related to recent computational challenges that arise in the medical field, including but not limited to clinical data analysis, interpretable machine learning, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are on the list of selected papers. | ||||||||||||||
| Prerequisites / Notice | Knowledge of machine learning and interest in applications in medicine. ML4H is beneficial as a prior course. | ||||||||||||||
| 263-5702-00L | Seminar on Digital Humans Number of participants limited to 24. The deadline for deregistering expires at the end of the third 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 | M. Gross, B. Solenthaler, S. Tang, R. Wampfler | ||||||||||
| Abstract | This seminar covers advanced topic in digital humans with a focus on the latest research results. Topics include estimating human pose and motion from images, human motion synthesis, learning-based human avatar creation, learning neural implicit representations for humans, modeling, animations, artificial intelligence for digital characters, and others. A collection of research papers is selected. | ||||||||||||||
| Learning objective | The goal is to get an overview of actual research topics in the field of digital humans and to improve presentation and critical analysis skills. | ||||||||||||||
| Content | This seminar covers advanced topics in digital humans including both seminal research papers as well as the latest research results. A collection of research papers are selected covering topics such as estimating human pose and motion from images, human motion synthesis, learning-based human avatar creation, learning neural implicit representations for humans, modeling, animations, artificial intelligence for digital characters, and others. Each student presents one paper to the class and leads a discussion about the paper. All students read the papers and participate in the discussion. | ||||||||||||||
| Literature | Individual research papers are selected each term. See https://vlg.inf.ethz.ch/ and http://graphics.ethz.ch/ for example papers. | ||||||||||||||
| 227-2211-00L | Seminar in Computer Architecture Number of participants limited to 28. 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 | O. Mutlu, M. H. K. Alser, J. Gómez Luna | ||||||||||
| Abstract | In this seminar course, we will cover fundamental and cutting-edge research papers in computer architecture. The course will consist of multiple components that are aimed at improving students' technical skills in computer architecture, critical thinking and analysis on computer architecture concepts, as well as technical presentation of concepts and papers in both spoken and written forms. | ||||||||||||||
| Learning objective | The 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 lecture, participate in the discussion, and create a synthesis report at the end of the course. | ||||||||||||||
| Content | Topics will center around computer architecture. We will, for example, discuss papers on hardware security; new execution paradigms like processing in memory; architectural acceleration mechanisms for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; various fundamental and emerging paradigms in computer architecture; hardware/software co-design and cooperation; fault tolerance; energy efficiency; heterogeneous and parallel systems; technology scaling; new execution models, etc. See https://safari.ethz.ch/architecture_seminar for past examples. | ||||||||||||||
| Lecture notes | All the materials will be posted on the course website: https://safari.ethz.ch/architecture_seminar/ Links to past course materials, including the synthesis report assignment, can be found in this page: https://safari.ethz.ch/architecture_seminar | ||||||||||||||
| Literature | Key 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. See https://safari.ethz.ch/architecture_seminar for past examples. | ||||||||||||||
| Prerequisites / Notice | Design of Digital Circuits. Students should have done very well in Digital Design and Computer Architecture (https://safari.ethz.ch/digitaltechnik) show a genuine interest in Computer Architecture research and practice. | ||||||||||||||
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