Search result: Catalogue data in Autumn Semester 2020
Computer Science Master | ||||||
Master Studies (Programme Regulations 2009) | ||||||
Focus Courses | ||||||
Focus Courses General Studies | ||||||
Focus Elective Courses General Studies | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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227-0575-00L | Advanced Topics in Communication Networks (Autumn 2020) | W | 6 credits | 2V + 2U | L. Vanbever | |
Abstract | This course covers advanced topics and technologies in computer networks, both theoretically and practically. It is offered each Fall semester, with rotating topics. Repetition for credit is possible with consent of the instructor. In the Fall 2020, the course will cover advanced topics in Internet routing and forwarding. | |||||
Objective | The goals of this course is to provide students with a deeper understanding of the existing and upcoming Internet routing and forwarding technologies used in large-scale computer networks such as Internet Service Providers (e.g., Swisscom or Deutsche Telekom), Content Delivery Networks (e.g., Netflix) and Data Centers (e.g., Google). Besides covering the fundamentals, the course will be “hands-on” and will enable students to play with the technologies in realistic network environments, and even implement some of them on their own during labs and a final group project. | |||||
Content | The course will cover advanced topics in Internet routing and forwarding such as: - Tunneling - Hierarchical routing - Traffic Engineering and Load Balancing - Virtual Private Networks - Quality of Service/Queuing/Scheduling - IP Multicast - Fast Convergence - Network virtualization - Network programmability (OpenFlow, P4) - Network measurements The course will be divided in two main blocks. The first block (~10 weeks) will interleave classical lectures with practical exercises and labs. The second block (~4 weeks) will consist of a practical project which will be performed in small groups (~3 students). During the second block, lecture slots will be replaced by feedback sessions where students will be able to ask questions and get feedback about their project. The last week of the semester will be dedicated to student presentations and demonstrations. | |||||
Lecture notes | Lecture notes and material will be made available before each course on the course website. | |||||
Literature | Relevant references will be made available through the course website. | |||||
Prerequisites / Notice | Prerequisites: Communication Networks (227-0120-00L) or equivalents / good programming skills (in any language) are expected as both the exercices and the final project will involve coding. | |||||
401-3054-14L | Probabilistic Methods in Combinatorics | W | 6 credits | 2V + 1U | B. Sudakov | |
Abstract | This course provides a gentle introduction to the Probabilistic Method, with an emphasis on methodology. We will try to illustrate the main ideas by showing the application of probabilistic reasoning to various combinatorial problems. | |||||
Objective | ||||||
Content | The topics covered in the class will include (but are not limited to): linearity of expectation, the second moment method, the local lemma, correlation inequalities, martingales, large deviation inequalities, Janson and Talagrand inequalities and pseudo-randomness. | |||||
Literature | - The Probabilistic Method, by N. Alon and J. H. Spencer, 3rd Edition, Wiley, 2008. - Random Graphs, by B. Bollobás, 2nd Edition, Cambridge University Press, 2001. - Random Graphs, by S. Janson, T. Luczak and A. Rucinski, Wiley, 2000. - Graph Coloring and the Probabilistic Method, by M. Molloy and B. Reed, Springer, 2002. | |||||
401-3901-00L | Mathematical Optimization | W | 11 credits | 4V + 2U | R. Zenklusen | |
Abstract | Mathematical treatment of diverse optimization techniques. | |||||
Objective | The goal of this course is to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on this structural understanding. | |||||
Content | Key topics include: - Linear programming and polyhedra; - Flows and cuts; - Combinatorial optimization problems and techniques; - Equivalence between optimization and separation; - Brief introduction to Integer Programming. | |||||
Literature | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes. - Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986. | |||||
Prerequisites / Notice | Solid background in linear algebra. | |||||
401-4521-70L | Geometric Tomography - Uniqueness, Statistical Reconstruction and Algorithms | W | 4 credits | 2V | J. Hörrmann | |
Abstract | Self-contained course on the theoretical aspects of the reconstruction of geometric objects from tomographic projection and section data. | |||||
Objective | Introduction to geometric tomography and understanding of various theoretical aspects of reconstruction problems. | |||||
Content | The problem of reconstruction of an object from geometric information like X-ray data is a classical inverse problem on the overlap between applied mathematics, statistics, computer science and electrical engineering. We focus on various aspects of the problem in the case of prior shape information on the reconstruction object. We will answer questions on uniqueness of the reconstruction and also cover statistical and algorithmic aspects. | |||||
Literature | R. Gardner: Geometric Tomography F. Natterer: The Mathematics of Computerized Tomography A. Rieder: Keine Probleme mit inversen Problemen | |||||
Prerequisites / Notice | A sound mathematical background in geometry, analysis and probability is required though a repetition of relevant material will be included. The ability to understand and write mathematical proofs is mandatory. | |||||
636-0017-00L | Computational Biology | W | 6 credits | 3G + 2A | T. Stadler, T. Vaughan | |
Abstract | The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced. | |||||
Objective | Attendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are: * stochastic models in molecular evolution * phylogenetic & phylodynamic inference * maximum likelihood and Bayesian statistics Attendees will apply these concepts to a number of applications yielding biological insight into: * epidemiology * pathogen evolution * macroevolution of species | |||||
Content | The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises. | |||||
Lecture notes | Lecture slides will be available on moodle. | |||||
Literature | The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Yang, Z. 2006. Computational Molecular Evolution. * Felsenstein, J. 2004. Inferring Phylogenies. * Semple, C. & Steel, M. 2003. Phylogenetics. * Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST. | |||||
Prerequisites / Notice | Basic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date Link For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course. | |||||
Seminar in General Studies | ||||||
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 | 3 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. | |||||
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 | Succesful 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 | |
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. | |||||
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, G. Rätsch, J. Vogt, F. Yang | |
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. | |||||
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 | Advanced Topics in Computer Graphics and Vision 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 | M. Gross, M. Pollefeys, O. Sorkine Hornung, S. Tang | |
Abstract | This seminar covers advanced topics in computer graphics, such as modeling, rendering, animation, real-time graphics, physical simulation, and computational photography. 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. | |||||
Objective | The goal is to get an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentations and critical analysis skills. | |||||
Content | This seminar covers advanced topics in computer graphics, including both seminal research papers as well as the latest research results. Each time the course is offered, a collection of research papers are selected covering topics such as modeling, rendering, animation, real-time graphics, physical simulation, and computational photography. 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. See Link for the current list. | |||||
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. | |||||
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 | Organizational note: the seminar will meet only when there is a scheduled presentation. Please consult the seminar's home page for information. | |||||
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. |
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