Search result: Catalogue data in Autumn Semester 2020
Computer Science Master | ||||||
Master Studies (Programme Regulations 2009) | ||||||
Focus Courses | ||||||
Focus Courses General Studies | ||||||
Seminar in General Studies | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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227-2211-00L | Seminar in Computer Architecture 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 | O. Mutlu, M. H. K. Alser, J. Gómez Luna | |
Abstract | This seminar course covers fundamental and cutting-edge research papers in computer architecture. It consists of 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. | |||||
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 meeting, 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; 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 notes | All materials will be posted on the course website: Link Past course materials, including the synthesis report assignment, can be found in the Spring 2020 website for the course: Link | |||||
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. | |||||
Prerequisites / Notice | Digital Design and Computer Architecture. Students should (1) have done very well in Digital Design and Computer Architecture and (2) show a genuine interest in Computer Architecture. | |||||
263-2926-00L | Deep Learning for Big Code 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 | V. Raychev | |
Abstract | The seminar covers some of the latest and most exciting developments (industrial and research) in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities. | |||||
Objective | The objective of the seminar is to: - Introduce students to the field of Deep Learning for Big Code. - Learn how machine learning models can be used to solve practical challenges in software engineering and programming beyond traditional methods. - Highlight the latest research and work opportunities in industry and academia available on this topic. | |||||
Content | The last 5 years have seen increased interest in applying advanced machine learning techniques such as deep learning to new kind of data: program code. As the size of open source code increases dramatically (over 980 billion lines of code written by humans), so comes the opportunity for new kind of deep probabilistic methods and commercial systems that leverage this data to revolutionize software creation and address hard problems not previously possible. Examples include: machines writing code, program de-obfuscation for security, code search, and many more. Interestingly, this new type of data, unlike natural language and images, introduces technical challenges not typically encountered when working with standard datasets (e.g., images, videos, natural language), for instance, finding the right representation over which deep learning operates. This in turn has the potential to drive new kinds of machine learning models with broad applicability. Because of this, there has been substantial interest over the last few years in both industry (e.g., companies such as Facebook starting, various start-ups in the space such as Link), academia (e.g., Link) and government agencies (e.g., DARPA) on using machine learning to automate various programming tasks. In this seminar, we will cover some of the latest and most exciting developments in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities. The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation. | |||||
Prerequisites / Notice | The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation. The seminar is ideally suited for M.Sc. students in Computer Science. | |||||
263-3504-00L | Hardware Acceleration for Data Processing 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, A. Klimovic, C. Zhang | |
Abstract | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | |||||
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. | |||||
263-3608-00L | Digitalization and the Rebound Effect 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 | V. C. Coroama | |
Abstract | Digitalization is hailed as a silver bullet towards environmental sustainability. Via optimizations or substitutions, it can lead to large reductions of GHG emissions and energy use. These gains, however, bear at their core the poisoned gift of rebound effects. The seminar will highlight the interplay between digitalization-induced environmental benefits and their rebound-based countereffects. | |||||
Objective | Learn about the impact of digitalization on energy consumption, greenhouse gas emissions, and environmental sustainability in general, with special emphasis on the subtler implications of rebound effects. Learn to review scientific literature, to deliver a scientifically sound presentation respecting the allocated time, and to produce a scientific report. | |||||
Content | In recent years, “digitalization” became a widely discussed phenomenon in popular media. In business contexts, it now stands for the broad use of digital information and communication technology (ICT), and the subsequent induced change in business operations or whole business models (“digital transformation”). This ongoing process encompasses technological developments such as distributed sensing, ubiquitous wireless communication, the Internet of things, big data, machine learning, artificial intelligence, augmented and virtual reality, 3D printing, robotics, or automation. Through its ubiquitous and profound effects, digitalization is often restructuring or disrupting economic processes and social practices. Given its vast capabilities, digitalization is frequently hailed as a key ingredient towards environmental sustainability. By optimizing existing processes or substituting them altogether, digitalization can lead to substantial reductions of carbon emissions as well as energy and resource use. Despite this potential, however, the sometimes spectacular efficiency gains induced by digitalization bear at their very core the poisoned gift of rebound effects. In economics, “rebound effects” are an umbrella term defining a variety of mechanisms that reduce or even overcompensate the savings from improved energy or material efficiency. In a nutshell, positive initial effects make a product more attractive (through lower prices or added benefits), which is in turn likely to spur demand for that same good or service (which became more attractive), or also for other products due to the increased disposable income or time. This seminar will highlight selected aspects of this interplay between digitalization-induced environmental benefits and their rebound-based countereffects. The first two presentations will introduce digitalization and (the several types of) rebound effects, respectively. After analyzing the mechanisms by which digitalization can bring about environmental benefits, a couple of presentations will compare environmental chances and perils in several domains enabled or deeply affected by digitalization: teleworking, e-commerce, sharing economy (e.g. Uber, Airbnb, bicycle sharing), autonomous driving, last-minute booking, and just-in-time production. | |||||
Literature | Will be announced at the beginning of the semester for each topic. | |||||
Prerequisites / Notice | An introduction to the seminar will be given Thursday, September 17th, 2020, during the first class. Seminar topics will be assigned to students during this session. Due to the large expected number of interested students, this first class will be held online. Please check Link for further information’ | |||||
263-3900-01L | Communication Networks Seminar Number of participants limited to 20. 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 | A. Singla, L. Vanbever | |
Abstract | We explore recent advances in networking by reading high quality research papers, and discussing open research opportunities, most of which are suitable for students to later take up as thesis or semester projects. | |||||
Objective | The objectives are (a) to understand the state-of-the-art in the field; (b) to learn to read, present and critique papers; (c) to engage in discussion and debate about research questions; and (d) to identify opportunities for new research. Students are expected to attend the entire seminar, choose a topic for presentation from a given list, make a presentation on that topic, and lead the discussion. Further, for each reading, every student needs to submit a review before the in-class discussion. Students are evaluated on their submitted reviews, their presentation and discussion leadership, and participation in seminar discussions. | |||||
Literature | A program will be posted here: Link, comprising of a list of papers the seminar group will cover. | |||||
Prerequisites / Notice | An undergraduate-level understanding of networking, such that the student is familiar with concepts like reliable transport protocols (like TCP) and basics of Internet routing. ETH courses that fulfill this requirement: Computer Networks (252-0064-00L) and Communication Networks (227-0120-00L). Similar courses at other universities are also sufficient. | |||||
263-5155-00L | Causal Representation Learning 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. Schölkopf | |
Abstract | Deep neural networks have achieved impressive success on prediction tasks in a supervised learning setting, provided sufficient labelled data is available. However, current AI systems lack a versatile understanding of the world around us, as shown in a limited ability to transfer and generalize between tasks. | |||||
Objective | The goal of this class is for students to gain experience with advanced research at the intersection of causal inference and deep learning. | |||||
Content | The course focuses on challenges and opportunities between deep learning and causal inference, and highlights work that attempts to develop statistical representation learning towards interventional/causal world models. The course will include guest lectures from renowned scientist both from academia as well as top industrial research labs. Deep Representation Learning, Causal Structure Learning, Disentangled Representations, Independent Mechanisms, Causal Inference, World Models and Interactive Learning. | |||||
Prerequisites / Notice | BSc in Computer Science or related field (e.g. Mathematics, Physics) and passed at least one learning course e.g. Intro to Machine Learning or Probabilistic Artificial Intelligence. | |||||
Computer Science Elective Courses The Elective Computer Science Courses can be selected from all Master level courses offered by D-INFK. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
252-0293-00L | Wireless Networking and Mobile Computing | W | 4 credits | 2V + 1U | S. Mangold | |
Abstract | This course gives an overview about wireless standards and summarizes the state of art for Wi-Fi 802.11, Cellular 5G, and Internet-of-Things, including new topics such as contact tracing with Bluetooth, audio communication, cognitive radio, visible light communications. The course combines lectures with a set of assignments in which students are asked to work with a JAVA simulation tool. | |||||
Objective | The objective of the course is to learn about the general principles of wireless communications, including physics, frequency spectrum regulation, and standards. Further, the most up-to-date standards and protocols used for wireless LAN IEEE 802.11, Wi-Fi, Internet-of-Things, sensor networks, cellular networks, visible light communication, and cognitive radios, are analyzed and evaluated. Students develop their own add-on mobile computing algorithms to improve the behavior of the systems, using a Java-based event-driven simulator. We also hand out embedded systems that can be used for experiments for optical communication. | |||||
Content | New: Starting 2020, we will address contact tracing, radio link budget, location distance measurements, and Bluetooth in more depth. Wireless Communication, Wi-Fi, Contact Tracing, Bluetooth, Internet-of-Things, 5G, Standards, Regulation, Algorithms, Radio Spectrum, Cognitive Radio, Mesh Networks, Optical Communication, Visible Light Communication | |||||
Lecture notes | The course material will be made available by the lecturer. | |||||
Literature | (1) The course webpage (look for Stefan Mangold's site) (2) The Java 802 protocol emulator "JEmula802" from Link (3) WALKE, B. AND MANGOLD, S. AND BERLEMANN, L. (2006) IEEE 802 Wireless Systems Protocols, Multi-Hop Mesh/Relaying, Performance and Spectrum Coexistence. New York U.S.A.: John Wiley & Sons. Nov 2006. (4) BERLEMANN, L. AND MANGOLD, S. (2009) Cognitive Radio for Dynamic Spectrum Access . New York U.S.A.: John Wiley & Sons. Jan 2009. (5) MANGOLD, S. ET.AL. (2003) Analysis of IEEE 802.11e for QoS Support in Wireless LANs. IEEE Wireless Communications, vol 10 (6), 40-50. | |||||
Prerequisites / Notice | Students should have interest in wireless communication, and should be familiar with Java programming. Experience with GNU Octave or Matlab will help too (not required). | |||||
263-0600-00L | Research in Computer Science Only for Computer Science MSc. | W | 5 credits | 11A | Professors | |
Abstract | Independent project work under the supervision of a Computer Science Professor. | |||||
Objective | Independent project work under the supervision of a Computer Science Professor. | |||||
Prerequisites / Notice | Only students who fulfill one of the following requirements are allowed to begin a research project: a) 1 lab (interfocus course) and 1 focus course b) 2 core focus courses c) 2 labs (interfocus courses) A task description must be submitted to the Student Administration Office at the beginning of the work. | |||||
227-0423-00L | Neural Network Theory | W | 4 credits | 2V + 1U | H. Bölcskei | |
Abstract | The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, basics of approximation theory, fundamental limits of deep neural network learning, geometry of decision surfaces, capacity of separating surfaces, dimension measures relevant for generalization, VC dimension of neural networks. | |||||
Objective | After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of (deep) neural networks. | |||||
Content | 1. Universal approximation with single- and multi-layer networks 2. Introduction to approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, non-linear approximation theory 3. Fundamental limits of deep neural network learning 4. Geometry of decision surfaces 5. Separating capacity of nonlinear decision surfaces 6. Dimension measures: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis (VC) dimension 7. Dimensions of neural networks 8. Generalization error in neural network learning | |||||
Lecture notes | Detailed lecture notes will be provided. | |||||
Prerequisites / Notice | This course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular. | |||||
227-0781-00L | Low-Power System Design Does not take place this semester. | W | 6 credits | 2V + 2U | ||
Abstract | Introduction to low-power and low-energy design techniques from a systems perspective including aspects both from hard- and software. The focus of this lecture is on cutting across a number of related fields discussing architectural concepts, modeling and measurement techniques as well as software design mainly using the example of networked embedded systems. | |||||
Objective | Knowledge of the state-of-the-art in low power system design, understanding recent research results and their implication on industrial products. | |||||
Content | Designing systems with a low energy footprint is an increasingly important. There are many applications for low-power systems ranging from mobile devices powered from batteries such as today's smart phones to energy efficient household appliances and datacenters. Key drivers are to be found mainly in the tremendous increase of mobile devices and the growing integration density requiring to carefully reason about power, both from a provision and consumption viewpoint. Traditional circuit design classes introduce low-power solely from a hardware perspective with a focus on the power performance of a single or at most a hand full of circuit elements. Similarly, low-power aspects are touched in a multitude of other classes, mostly as a side topic. However in successfully designing systems with a low energy footprint it is not sufficient to only look at low-power as an aspect of second class. In modern low-power system design advanced CMOS circuits are of course a key ingredient but successful low-power integration involves many more disciplines such as system architecture, different sources of energy as well as storage and most importantly software and algorithms. In this lecture we will discuss aspects of low-power design as a first class citizen introducing key concepts as well as modeling and measurement techniques focusing mainly on the design of networked embedded systems but of course equally applicable to many other classes of systems. The lecture is further accompanied by a reading seminar as well as exercises and lab sessions. | |||||
Lecture notes | Exercise and lab materials, copies of lecture slides. | |||||
Literature | A detailed reading list will be made available in the lecture. | |||||
Prerequisites / Notice | Knowledge in embedded systems, system software, (wireless) networking, possibly integrated circuits, and hardware software codesign. | |||||
Internship | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
252-0700-00L | Internship Only for Computer Science MSc. | W | 0 credits | external organisers | ||
Abstract | An internship provides opportunities to gain experience in an industrial environment and creates a network of contacts. | |||||
Objective | The main objective of the iinternship is to expose students to the industrial work environment. During this period, students have the opportunity to be involved in on-going projects at the host institution. | |||||
Content | Internship in a computer science company, which is admitted by the CS Department at ETH. Minimum 10 weeks fulltime employment. | |||||
Prerequisites / Notice | To register the internship, please submit a document to the Student Administration Office containing the following information at the latest two weeks after beginning the intership: - a detailed task description: task, technologies, milestones etc. - start and end date of the internship - supervisor: name and academic degree | |||||
Elective Courses (only for Programme Regulations 2009) Students can individually chose from the entire Master course offerings from ETH Zurich, EPF Lausanne, the University of Zurich and - but only with the consent of the Director of Studies - from all other Swiss universities. For further details, refer to Art. 31 of the Regulations 2009 for the Master Program in Computer Science. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
263-0610-00L | Direct Doctorate Research Project Only for Direct Doctorate Students | O | 15 credits | 23A | Professors | |
Abstract | Direct Doctorate Students join a research group of D-INFK in order to acquire a broader view of the different research groups and areas. | |||||
Objective | Students extend their knowledge of the different research topics and improve their scientific approach of working on an actual research project. | |||||
Content | 2nd semester students join a research group of D-INFK in order to acquire a broader view of the different research groups and areas. The research group chosen must not be identical with the one, in which the thesis project is conducted. | |||||
Prerequisites / Notice | Please be aware that the research project and the master's thesis have to be coached by two different research groups! | |||||
263-0620-00L | Direct Doctorate Research Plan Only for Direct Doctorate Students | O | 15 credits | 23A | Professors | |
Abstract | The research plan aims at planning and structuring a student's research work and thesis. It further contributes to the student's ability to write research proposals. | |||||
Objective | The student has to present the research plan to the faculty members in order to defend his/her research goals, but also to demonstrate a solid knowledge on the background literature as well as the planned and alternative procedures to follow. | |||||
GESS Science in Perspective Note that no more than six credits can be accredited in this category | ||||||
» see GESS Science in Perspective: Language Courses ETH/UZH | ||||||
» see GESS Science in Perspective: Type A: Enhancement of Reflection Capability | ||||||
» Recommended GESS Science in Perspective (Type B) for D-INFK. | ||||||
Master's Thesis | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
263-0800-00L | Master's Thesis Only students who fulfill the following criteria are allowed to begin with their master thesis: a. successful completion of the bachelor programme; b. fulfilling any additional requirements necessary to gain admission to the master programme; c. "Inter focus courses" (12 credits) completed; d. "Focus courses" (26 credits) completed. | O | 30 credits | 64D | Supervisors | |
Abstract | The Master's thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working. | |||||
Objective | To work independently and to produce a scientifically structured work under the supervision of a Computer Science Professor. | |||||
Content | Independent project work supervised by a Computer Science professor. Duration 6 months. | |||||
Prerequisites / Notice | Supervisor must be a professor at D-INFK or affiliated, see Link |
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