# Search result: Catalogue data in Spring Semester 2020

Computer Science Master | ||||||

Focus Courses | ||||||

Focus Courses General Studies | ||||||

Core Focus Courses General Studies | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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252-0538-00L | Shape Modeling and Geometry Processing | W | 6 credits | 2V + 1U + 2A | O. Sorkine Hornung | |

Abstract | This course covers the fundamentals and some of the latest developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and polygonal meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing, topics in digital shape fabrication. | |||||

Objective | The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and geometry processing. | |||||

Content | Recent advances in 3D geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers the fundamentals and some of the latest developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and triangle meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing and digital shape fabrication. | |||||

Lecture notes | Slides and course notes | |||||

Prerequisites / Notice | Prerequisites: Visual Computing, Computer Graphics or an equivalent class. Experience with C++ programming. Solid background in linear algebra and analysis. Some knowledge of differential geometry, computational geometry and numerical methods is helpful but not a strict requirement. | |||||

261-5110-00L | Optimization for Data Science | W | 8 credits | 3V + 2U + 2A | B. Gärtner, D. Steurer | |

Abstract | This course provides an in-depth theoretical treatment of optimization methods that are particularly relevant in data science. | |||||

Objective | Understanding the theoretical guarantees (and their limits) of relevant optimization methods used in data science. Learning general paradigms to deal with optimization problems arising in data science. | |||||

Content | This course provides an in-depth theoretical treatment of optimization methods that are particularly relevant in machine learning and data science. In the first part of the course, we will first give a brief introduction to convex optimization, with some basic motivating examples from machine learning. Then we will analyse classical and more recent first and second order methods for convex optimization: gradient descent, projected gradient descent, subgradient descent, stochastic gradient descent, Nesterov's accelerated method, Newton's method, and Quasi-Newton methods. The emphasis will be on analysis techniques that occur repeatedly in convergence analyses for various classes of convex functions. We will also discuss some classical and recent theoretical results for nonconvex optimization. In the second part, we discuss convex programming relaxations as a powerful and versatile paradigm for designing efficient algorithms to solve computational problems arising in data science. We will learn about this paradigm and develop a unified perspective on it through the lens of the sum-of-squares semidefinite programming hierarchy. As applications, we are discussing non-negative matrix factorization, compressed sensing and sparse linear regression, matrix completion and phase retrieval, as well as robust estimation. | |||||

Prerequisites / Notice | As background, we require material taught in the course "252-0209-00L Algorithms, Probability, and Computing". It is not necessary that participants have actually taken the course, but they should be prepared to catch up if necessary. | |||||

263-2925-00L | Program Analysis for System Security and Reliability | W | 6 credits | 2V + 1U + 2A | P. Tsankov | |

Abstract | Security issues in modern systems (blockchains, datacenters, AI) result in billions of losses due to hacks. This course introduces the security issues in modern systems and state-of-the-art automated techniques for building secure and reliable systems. The course has a practical focus and covers systems built by successful ETH spin-offs. | |||||

Objective | * Learn about security issues in modern systems -- blockchains, smart contracts, AI-based systems (e.g., autonomous cars), data centers -- and why they are challenging to address. * Understand how the latest automated analysis techniques work, both discrete and probabilistic. * Understand how these techniques combine with machine-learning methods, both supervised and unsupervised. * Understand how to use these methods to build reliable and secure modern systems. * Learn about new open problems that if solved can lead to research and commercial impact. | |||||

Content | Part I: Security of Blockchains - We will cover existing blockchains (e.g., Ethereum, Bitcoin), how they work, what the core security issues are, and how these have led to massive financial losses. - We will show how to extract useful information about smart contracts and transactions using interactive analysis frameworks for querying blockchains (e.g. Google's Ethereum BigQuery). - We will discuss the state-of-the-art security tools (e.g., https://securify.ch) for ensuring that smart contracts are free of security vulnerabilities. - We will study the latest automated reasoning systems (e.g., https://verx.ch) for checking custom (temporal) properties of smart contracts and illustrate their operation on real-world use cases. - We will study the underlying methods for automated reasoning and testing (e.g., abstract interpretation, symbolic execution, fuzzing) are used to build such tools. Part II: Security of Datacenters and Networks - We will show how to ensure that datacenters and ISPs are secured using declarative reasoning methods (e.g., Datalog). We will also see how to automatically synthesize secure configurations (e.g. using SyNET and NetComplete) which lead to desirable behaviors, thus automating the job of the network operator and avoiding critical errors. - We will discuss how to apply modern discrete probabilistic inference (e.g., PSI and Bayonet) so to reason about probabilistic network properties (e.g., the probability of a packet reaching a destination if links fail). Part III: Machine Learning for Security - We will discuss how machine learning models for structured prediction are used to address security tasks, including de-obfuscation of binaries (Debin: https://debin.ai), Android APKs (DeGuard: http://apk-deguard.com) and JavaScript (JSNice: http://jsnice.org). - We will study to leverage program abstractions in combination with clustering techniques to learn security rules for cryptography APIs from large codebases. - We will study how to automatically learn to identify security vulnerabilities related to the handling of untrusted inputs (cross-Site scripting, SQL injection, path traversal, remote code execution) from large codebases. To gain a deeper understanding, the course will involve a hands-on programming project where the methods studied in the class will be applied. | |||||

263-3800-00L | Advanced Operating Systems | W | 7 credits | 2V + 2U + 2A | D. Cock, T. Roscoe | |

Abstract | This course is intended to give students a thorough understanding of design and implementation issues for modern operating systems, with a particular emphasis on the challenges of modern hardware features. We will cover key design issues in implementing an operating system, such as memory management, scheduling, protection, inter-process communication, device drivers, and file systems. | |||||

Objective | The goals of the course are, firstly, to give students: 1. A broader perspective on OS design than that provided by knowledge of Unix or Windows, building on the material in a standard undergraduate operating systems class 2. Practical experience in dealing directly with the concurrency, resource management, and abstraction problems confronting OS designers and implementers 3. A glimpse into future directions for the evolution of OS and computer hardware design | |||||

Content | The course is based on practical implementation work, in C and assembly language, and requires solid knowledge of both. The work is mostly carried out in teams of 3-4, using real hardware, and is a mixture of team milestones and individual projects which fit together into a complete system at the end. Emphasis is also placed on a final report which details the complete finished artifact, evaluates its performance, and discusses the choices the team made while building it. | |||||

Prerequisites / Notice | The course is based around a milestone-oriented project, where students work in small groups to implement major components of a microkernel-based operating system. The final assessment will be a combination grades awarded for milestones during the course of the project, a final written report on the work, and a set of test cases run on the final code. | |||||

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. | |||||

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 | Ideally, students will have taken the D-INFK Bachelors course “Information Security" or an equivalent course at Bachelors level. | |||||

227-0558-00L | Principles of Distributed Computing | W | 7 credits | 2V + 2U + 2A | R. Wattenhofer, M. Ghaffari | |

Abstract | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||

Objective | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||

Content | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||

Lecture notes | Available. Our course script is used at dozens of other universities around the world. | |||||

Literature | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||

Prerequisites / Notice | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||

401-3632-00L | Computational Statistics | W | 8 credits | 3V + 1U | M. H. Maathuis | |

Abstract | We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R. | |||||

Objective | The student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R. | |||||

Content | See the class website | |||||

Prerequisites / Notice | At least one semester of (basic) probability and statistics. Programming experience is helpful but not required. | |||||

Elective Focus Courses General Studies | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0312-00L | Ubiquitous Computing | W | 4 credits | 2V + 1A | C. Holz, F. Mattern, S. Mayer | |

Abstract | Unlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses. Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication. | |||||

Objective | Unlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses. Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication. | |||||

Lecture notes | Copies of slides will be made available | |||||

Literature | Will be provided in the lecture. To put you in the mood: Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104 | |||||

252-0408-00L | Cryptographic Protocols | W | 6 credits | 2V + 2U + 1A | M. Hirt, U. Maurer | |

Abstract | The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc. | |||||

Objective | Indroduction to a very active research area with many gems and paradoxical results. Spark interest in fundamental problems. | |||||

Content | The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc. | |||||

Lecture notes | the lecture notes are in German, but they are not required as the entire course material is documented also in other course material (in english). | |||||

Prerequisites / Notice | A basic understanding of fundamental cryptographic concepts (as taught for example in the course Information Security or in the course Cryptography Foundations) is useful, but not required. | |||||

252-0437-00L | Distributed Algorithms | W | 5 credits | 3V + 1A | F. Mattern | |

Abstract | Models of distributed computations, time space diagrams, virtual time, logical clocks and causality, wave algorithms, parallel and distributed graph traversal, consistent snapshots, mutual exclusion, election and symmetry breaking, distributed termination detection, garbage collection in distributed systems, monitoring distributed systems, global predicates. | |||||

Objective | Become acquainted with models and algorithms for distributed systems. | |||||

Content | Verteilte Algorithmen sind Verfahren, die dadurch charakterisiert sind, dass mehrere autonome Prozesse gleichzeitig Teile eines gemeinsamen Problems in kooperativer Weise bearbeiten und der dabei erforderliche Informationsaustausch ausschliesslich über Nachrichten erfolgt. Derartige Algorithmen kommen im Rahmen verteilter Systeme zum Einsatz, bei denen kein gemeinsamer Speicher existiert und die Übertragungszeit von Nachrichten i.a. nicht vernachlässigt werden kann. Da dabei kein Prozess eine aktuelle konsistente Sicht des globalen Zustands besitzt, führt dies zu interessanten Problemen. Im einzelnen werden u.a. folgende Themen behandelt: Modelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate. | |||||

Literature | - F. Mattern: Verteilte Basisalgorithmen, Springer-Verlag - G. Tel: Topics in Distributed Algorithms, Cambridge University Press - G. Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition - A.D. Kshemkalyani, M. Singhal: Distributed Computing, Cambridge University Press - N. Lynch: Distributed Algorithms, Morgan Kaufmann Publ | |||||

252-0526-00L | Statistical Learning Theory | W | 7 credits | 3V + 2U + 1A | J. M. Buhmann, C. Cotrini Jimenez | |

Abstract | The course covers advanced methods of statistical learning: - Variational methods and optimization. - Deterministic annealing. - Clustering for diverse types of data. - Model validation by information theory. | |||||

Objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning. | |||||

Content | - Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing. - Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures. - Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation. - Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models. | |||||

Lecture notes | A draft of a script will be provided. Lecture slides will be made available. | |||||

Literature | Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||

Prerequisites / Notice | Knowledge of machine learning (introduction to machine learning and/or advanced machine learning) Basic knowledge of statistics. | |||||

252-0570-00L | Game Programming Laboratory In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum. | W | 10 credits | 9P | B. Sumner | |

Abstract | The goal of this course is the in-depth understanding of the technology and programming underlying computer games. Students gradually design and develop a computer game in small groups and get acquainted with the art of game programming. | |||||

Objective | The goal of this new course is to acquaint students with the technology and art of programming modern three-dimensional computer games. | |||||

Content | This course addresses modern three-dimensional computer game technology. During the course, small groups of students will design and develop a computer game. Focus will be put on technical aspects of game development, such as rendering, cinematography, interaction, physics, animation, and AI. In addition, we will cultivate creative thinking for advanced gameplay and visual effects. The "laboratory" format involves a practical, hands-on approach with traditional lectures. We will meet once a week to discuss technical issues and to track progress. For development we use MonoGames, which is a collection of libraries and tools that facilitate game development. While development will take place on PCs, we will ultimately deployour games on the Xbox One console. At the end of the course we will present our results to the public. | |||||

Lecture notes | Game Design Workshop: A Playcentric Approach to Creating Innovative Games by Tracy Fullerton | |||||

Prerequisites / Notice | The number of participants is limited. Prerequisites include: - Good programming skills (Java, C++, C#, etc.) - CG experience: Students should have taken, at a minimum, Visual Computing. Higher level courses are recommended, such as Introduction to Computer Graphics, Surface Representations and Geometric Modeling, and Physically-based Simulation in Computer Graphics. | |||||

252-0579-00L | 3D Vision | W | 5 credits | 3G + 1A | M. Pollefeys, V. Larsson | |

Abstract | The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization. | |||||

Objective | After attending this course, students will: 1. understand the core concepts for recovering 3D shape of objects and scenes from images and video. 2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications. 3. have a good overview over the current state-of-the art in 3D vision. 4. be able to critically analyze and asses current research in this area. | |||||

Content | The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications. | |||||

252-0817-00L | Distributed Systems Laboratory In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum. | W | 10 credits | 9P | G. Alonso, T. Hoefler, F. Mattern, A. Singla, R. Wattenhofer, C. Zhang | |

Abstract | This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. | |||||

Objective | Students acquire practical knowledge about technologies from the area of distributed systems. | |||||

Content | This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. The objecte of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course. For information of the course or projects available, please contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso. | |||||

252-1424-00L | Models of Computation | W | 6 credits | 2V + 2U + 1A | M. Cook | |

Abstract | This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more. | |||||

Objective | The goal of this course is to become acquainted with a wide variety of models of computation, to understand how models help us to understand the modeled systems, and to be able to develop and analyze models appropriate for new systems. | |||||

Content | This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more. | |||||

252-3005-00L | Natural Language Understanding Does not take place this semester. Takes place in HS20. | W | 5 credits | 2V + 1U + 1A | to be announced | |

Abstract | This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||

Objective | The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques. | |||||

Content | This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||

Literature | Lectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers. | |||||

252-5706-00L | Mathematical Foundations of Computer Graphics and Vision | W | 5 credits | 2V + 1U + 1A | M. R. Oswald, C. Öztireli | |

Abstract | This course presents the fundamental mathematical tools and concepts used in computer graphics and vision. Each theoretical topic is introduced in the context of practical vision or graphic problems, showcasing its importance in real-world applications. | |||||

Objective | The main goal is to equip the students with the key mathematical tools necessary to understand state-of-the-art algorithms in vision and graphics. In addition to the theoretical part, the students will learn how to use these mathematical tools to solve a wide range of practical problems in visual computing. After successfully completing this course, the students will be able to apply these mathematical concepts and tools to practical industrial and academic projects in visual computing. | |||||

Content | The theory behind various mathematical concepts and tools will be introduced, and their practical utility will be showcased in diverse applications in computer graphics and vision. The course will cover topics in sampling, reconstruction, approximation, optimization, robust fitting, differentiation, quadrature and spectral methods. Applications will include 3D surface reconstruction, camera pose estimation, image editing, data projection, character animation, structure-aware geometry processing, and rendering. | |||||

261-5120-00L | Machine Learning for Health Care Number of participants limited to 150. | W | 5 credits | 3P + 1A | G. Rätsch, J. Vogt, V. Boeva | |

Abstract | The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems. | |||||

Objective | During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions. | |||||

Content | The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: 1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc. 2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them. 3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them. 4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges. | |||||

Prerequisites / Notice | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II. | |||||

263-3501-00L | Future Internet | W | 6 credits | 1V + 1U + 3A | A. Singla | |

Abstract | This course will discuss recent advances in networking, with a focus on the Internet, with topics ranging from the algorithmic design of applications like video streaming to the likely near-future of satellite-based networking. | |||||

Objective | The goals of the course are to build on basic undergraduate-level networking, and provide an understanding of the tradeoffs and existing technology in the design of large, complex networked systems, together with concrete experience of the challenges through a series of lab exercises. | |||||

Content | The focus of the course is on principles, architectures, protocols, and applications used in modern networked systems. Example topics include: - How video streaming services like Netflix work, and research on improving their performance. - How Web browsing could be made faster - How the Internet's protocols are improving - Exciting developments in satellite-based networking (ala SpaceX) - The role of data centers in powering Internet services A series of programming assignments will form a substantial part of the course grade. | |||||

Lecture notes | Lecture slides will be made available at the course Web site: https://ndal.ethz.ch/courses/fi.html | |||||

Literature | No textbook is required, but there will be regularly assigned readings from research literature, liked to the course Web site: https://ndal.ethz.ch/courses/fi.html. | |||||

Prerequisites / Notice | An undergraduate class covering the basics of networking, such as Internet routing and TCP. At ETH, Computer Networks (252-0064-00L) and Communication Networks (227-0120-00L) suffice. Similar courses from other universities are acceptable too. | |||||

263-3710-00L | Machine Perception Number of participants limited to 200. | W | 5 credits | 2V + 1U + 1A | O. Hilliges | |

Abstract | Recent developments in neural networks (aka “deep learning”) have drastically advanced the performance of machine perception systems in a variety of areas including computer vision, robotics, and intelligent UIs. This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual tasks. | |||||

Objective | Students will learn about fundamental aspects of modern deep learning approaches for perception. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in learning-based computer vision, robotics and HCI. The final project assignment will involve training a complex neural network architecture and applying it on a real-world dataset of human activity. The core competency acquired through this course is a solid foundation in deep-learning algorithms to process and interpret human input into computing systems. In particular, students should be able to develop systems that deal with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others. | |||||

Content | We will focus on teaching: how to set up the problem of machine perception, the learning algorithms, network architectures and advanced deep learning concepts in particular probabilistic deep learning models The course covers the following main areas: I) Foundations of deep-learning. II) Probabilistic deep-learning for generative modelling of data (latent variable models, generative adversarial networks and auto-regressive models). III) Deep learning in computer vision, human-computer interaction and robotics. Specific topics include: I) Deep learning basics: a) Neural Networks and training (i.e., backpropagation) b) Feedforward Networks c) Timeseries modelling (RNN, GRU, LSTM) d) Convolutional Neural Networks for classification II) Probabilistic Deep Learning: a) Latent variable models (VAEs) b) Generative adversarial networks (GANs) c) Autoregressive models (PixelCNN, PixelRNN, TCNs) III) Deep Learning techniques for machine perception: a) Fully Convolutional architectures for dense per-pixel tasks (i.e., instance segmentation) b) Pose estimation and other tasks involving human activity c) Deep reinforcement learning IV) Case studies from research in computer vision, HCI, robotics and signal processing | |||||

Literature | Deep Learning Book by Ian Goodfellow and Yoshua Bengio | |||||

Prerequisites / Notice | This is an advanced grad-level course that requires a background in machine learning. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. The course will focus on state-of-the-art research in deep-learning and will not repeat basics of machine learning Please take note of the following conditions: 1) The number of participants is limited to 200 students (MSc and PhDs). 2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge 3) All practical exercises will require basic knowledge of Python and will use libraries such as TensorFlow, scikit-learn and scikit-image. We will provide introductions to TensorFlow and other libraries that are needed but will not provide introductions to basic programming or Python. The following courses are strongly recommended as prerequisite: * "Visual Computing" or "Computer Vision" The course will be assessed by a final written examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises. | |||||

263-4507-00L | Advances in Distributed Graph AlgorithmsDoes not take place this semester. | W | 6 credits | 3V + 1U + 1A | M. Ghaffari | |

Abstract | How can a network of computers solve the graph problems needed for running that network? | |||||

Objective | This course will familiarize the students with the algorithmic tools and techniques in local distributed graph algorithms, and overview the recent highlights in the field. This will also prepare the students for independent research at the frontier of this area. This is a special‐topics course in algorithm design. It should be accessible to any student with sufficient theoretical/algorithmic background. In particular, it assumes no familiarity with distributed computing. We only expect that the students are comfortable with the basics of algorithm design and analysis, as well as probability theory. It is possible to take this course simultaneously with the course “Principles of Distributed Computing”. If you are not sure whether you are ready for this class or not, please consult the instructor. | |||||

Content | How can a network of computers solve the graph problems needed for running that network? Answering this and similar questions is the underlying motivation of the area of Distributed Graph Algorithms. The area focuses on the foundational algorithmic aspects in these questions and provides methods for various distributed systems --- e.g., the Internet, a wireless network, a multi-processor computer, etc --- to solve computational problems that can be abstracted as graph problems. For instance, think about shortest path computation in routing, or about coloring and independent set computation in contention resolution. Over the past decade, we have witnessed a renaissance in the area of Distributed Graph Algorithms, with tremendous progress in many directions and solutions for a number of decades-old central problems. This course overviews the highlights of these results. The course will mainly focus on one half of the field, which revolves around locality and local problems. The other half, which relates to the issue of congestion and dealing with limited bandwidth in global problems, will not be addressed in this offering of the course. The course will cover a sampling of the recent developments (and open questions) at the frontier of research of distributed graph algorithms. The material will be based on a compilation of recent papers in this area, which will be provided throughout the semester. The tentative list of topics includes: - The shattering technique for local graph problems and its necessity - Lovasz Local Lemma algorithms, their distributed variants, and distributed applications - Distributed Derandomization - Distributed Lower bounds - Graph Coloring - Complexity Hierarchy and Gaps - Primal-Dual Techniques | |||||

Prerequisites / Notice | The class assumes no knowledge in distributed algorithms/computing. Our only prerequisite is the undergraduate class Algorithms, Probability, and Computing (APC) or any other course that can be seen as the equivalent. In particular, much of what we will discuss uses randomized algorithms and therefore, we will assume that the students are familiar with the tools and techniques in randomized algorithms and analysis (to the extent covered in the APC class). | |||||

263-4600-00L | Formal Methods for Information Security | W | 5 credits | 2V + 1U + 1A | R. Sasse, C. Sprenger | |

Abstract | The course focuses on formal methods for the modelling and analysis of security protocols for critical systems, ranging from authentication protocols for network security to electronic voting protocols and online banking. | |||||

Objective | The students will learn the key ideas and theoretical foundations of formal modelling and analysis of security protocols. The students will complement their theoretical knowledge by solving practical exercises, completing a small project, and using state-of-the-art tools. | |||||

Content | The course treats formal methods mainly for the modelling and analysis of security protocols. Cryptographic protocols (such as SSL/TLS, SSH, Kerberos, SAML single-sign on, and IPSec) form the basis for secure communication and business processes. Numerous attacks on published protocols show that the design of cryptographic protocols is extremely error-prone. A rigorous analysis of these protocols is therefore indispensable, and manual analysis is insufficient. The lectures cover the theoretical basis for the (tool-supported) formal modeling and analysis of such protocols. Specifically, we discuss their operational semantics, the formalization of security properties, and techniques and algorithms for their verification. In addition to the classical security properties for confidentiality and authentication, we will study strong secrecy and privacy properties. We will discuss electronic voting protocols, and RFID protocols (a staple of the Internet of Things), where these properties are central. The accompanying tutorials provide an opportunity to apply the theory and tools to concrete protocols. Moreover, we will discuss methods to abstract and refine security protocols and the link between symbolic protocol models and cryptographic models. Furthermore, we will also present a security notion for general systems based on non-interference as well as language-based information flow security where non-interference is enforced via a type system. | |||||

263-4400-00L | Advanced Graph Algorithms and Optimization Number of participants limited to 30. | W | 5 credits | 3G + 1A | R. Kyng | |

Abstract | This course will cover a number of advanced topics in optimization and graph algorithms. | |||||

Objective | The course will take students on a deep dive into modern approaches to graph algorithms using convex optimization techniques. By studying convex optimization through the lens of graph algorithms, students should develop a deeper understanding of fundamental phenomena in optimization. The course will cover some traditional discrete approaches to various graph problems, especially flow problems, and then contrast these approaches with modern, asymptotically faster methods based on combining convex optimization with spectral and combinatorial graph theory. | |||||

Content | Students should leave the course understanding key concepts in optimization such as first and second-order optimization, convex duality, multiplicative weights and dual-based methods, acceleration, preconditioning, and non-Euclidean optimization. Students will also be familiarized with central techniques in the development of graph algorithms in the past 15 years, including graph decomposition techniques, sparsification, oblivious routing, and spectral and combinatorial preconditioning. | |||||

Prerequisites / Notice | This course is targeted toward masters and doctoral students with an interest in theoretical computer science. Students should be comfortable with design and analysis of algorithms, probability, and linear algebra. Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not sure whether you're ready for this class or not, please consult the instructor. | |||||

263-4656-00L | Digital Signatures | W | 4 credits | 2V + 1A | D. Hofheinz | |

Abstract | Digital signatures as one central cryptographic building block. Different security goals and security definitions for digital signatures, followed by a variety of popular and fundamental signature schemes with their security analyses. | |||||

Objective | The student knows a variety of techniques to construct and analyze the security of digital signature schemes. This includes modularity as a central tool of constructing secure schemes, and reductions as a central tool to proving the security of schemes. | |||||

Content | We will start with several definitions of security for signature schemes, and investigate the relations among them. We will proceed to generic (but inefficient) constructions of secure signatures, and then move on to a number of efficient schemes based on concrete computational hardness assumptions. On the way, we will get to know paradigms such as hash-then-sign, one-time signatures, and chameleon hashing as central tools to construct secure signatures. | |||||

Literature | Jonathan Katz, "Digital Signatures." | |||||

Prerequisites / Notice | Ideally, students will have taken the D-INFK Bachelors course "Information Security" or an equivalent course at Bachelors level. | |||||

263-5300-00L | Guarantees for Machine Learning | W | 5 credits | 2V + 2A | F. Yang | |

Abstract | This course teaches classical and recent methods in statistics and optimization commonly used to prove theoretical guarantees for machine learning algorithms. The knowledge is then applied in project work that focuses on understanding phenomena in modern machine learning. | |||||

Objective | This course is aimed at advanced master and doctorate students who want to understand and/or conduct independent research on theory for modern machine learning. For this purpose, students will learn common mathematical techniques from statistical learning theory. In independent project work, they then apply their knowledge and go through the process of critically questioning recently published work, finding relevant research questions and learning how to effectively present research ideas to a professional audience. | |||||

Content | This course teaches some classical and recent methods in statistical learning theory aimed at proving theoretical guarantees for machine learning algorithms, including topics in - concentration bounds, uniform convergence - high-dimensional statistics (e.g. Lasso) - prediction error bounds for non-parametric statistics (e.g. in kernel spaces) - minimax lower bounds - regularization via optimization The project work focuses on active theoretical ML research that aims to understand modern phenomena in machine learning, including but not limited to - how overparameterization could help generalization ( interpolating models, linearized 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 ) - prediction with calibrated confidence ( conformal prediction, calibration ) | |||||

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”. It's also helpful to have heard an optimization course or approximation theoretic course. In addition to these prerequisites, this class requires a certain degree of mathematical maturity—including abstract thinking and the ability to understand and write proofs. | |||||

263-5806-00L | Computational Models of Motion for Character Animation and Robotics | W | 6 credits | 2V + 2U + 1A | S. Coros, M. Bächer, B. Thomaszewski | |

Abstract | This course covers fundamentals of physics-based modelling and numerical optimization from the perspective of character animation and robotics applications. The methods discussed in class derive their theoretical underpinnings from applied mathematics, control theory and computational mechanics, and they will be richly illustrated using examples ranging from locomotion controllers and crowd simula | |||||

Objective | Students will learn how to represent, model and algorithmically control the behavior of animated characters and real-life robots. The lectures are accompanied by programming assignments (written in C++) and a capstone project. | |||||

Content | Optimal control and trajectory optimization; multibody systems; kinematics; forward and inverse dynamics; constrained and unconstrained numerical optimization; mass-spring models for crowd simulation; FEM; compliant systems; sim-to-real; robotic manipulation of elastically-deforming objects. | |||||

Prerequisites / Notice | Experience with C++ programming, numerical linear algebra and multivariate calculus. Some background in physics-based modeling, kinematics and dynamics is helpful, but not necessary. | |||||

272-0300-00L | Algorithmics for Hard Problems Does not take place this semester. This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A. | W | 5 credits | 2V + 1U + 1A | ||

Abstract | This course unit looks into algorithmic approaches to the solving of hard problems, particularly with moderately exponential-time algorithms and parameterized algorithms. The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools. | |||||

Objective | To systematically acquire an overview of the methods for solving hard problems. To get deeper knowledge of exact and parameterized algorithms. | |||||

Content | First, the concept of hardness of computation is introduced (repeated for the computer science students). Then some methods for solving hard problems are treated in a systematic way. For each algorithm design method, it is discussed what guarantees it can give and how we pay for the improved efficiency. A special focus lies on moderately exponential-time algorithms and parameterized algorithms. | |||||

Lecture notes | Unterlagen und Folien werden zur Verfügung gestellt. | |||||

Literature | J. Hromkovic: Algorithmics for Hard Problems, Springer 2004. R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006. M. Cygan et al.: Parameterized Algorithms, 2015. F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010. | |||||

272-0302-00L | Approximation and Online Algorithms | W | 5 credits | 2V + 1U + 1A | H.‑J. Böckenhauer, D. Komm | |

Abstract | This lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches. | |||||

Objective | Get a systematic overview of different methods for designing approximative algorithms for hard optimization problems and online problems. Get to know methods for showing the limitations of these approaches. | |||||

Content | Approximation algorithms are one of the most succesful techniques to attack hard optimization problems. Here, we study the so-called approximation ratio, i.e., the ratio of the cost of the computed approximating solution and an optimal one (which is not computable efficiently). For an online problem, the whole instance is not known in advance, but it arrives pieceweise and for every such piece a corresponding part of the definite output must be given. The quality of an algorithm for such an online problem is measured by the competitive ratio, i.e., the ratio of the cost of the computed solution and the cost of an optimal solution that could be given if the whole input was known in advance. The contents of this lecture are - the classification of optimization problems by the reachable approximation ratio, - systematic methods to design approximation algorithms (e.g., greedy strategies, dynamic programming, linear programming relaxation), - methods to show non-approximability, - classic online problem like paging or scheduling problems and corresponding algorithms, - randomized online algorithms, - the design and analysis principles for online algorithms, and - limits of the competitive ratio and the advice complexity as a way to do a deeper analysis of the complexity of online problems. | |||||

Literature | The lecture is based on the following books: J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004 D. Komm: An Introduction to Online Computation: Determinism, Randomization, Advice, Springer, 2016 Additional literature: A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998 | |||||

401-3052-05L | Graph Theory | W | 5 credits | 2V + 1U | B. Sudakov | |

Abstract | Basic notions, trees, spanning trees, Caley's formula, vertex and edge connectivity, 2-connectivity, Mader's theorem, Menger's theorem, Eulerian graphs, Hamilton cycles, Dirac's theorem, matchings, theorems of Hall, König and Tutte, planar graphs, Euler's formula, basic non-planar graphs, graph colorings, greedy colorings, Brooks' theorem, 5-colorings of planar graphs | |||||

Objective | The students will get an overview over the most fundamental questions concerning graph theory. We expect them to understand the proof techniques and to use them autonomously on related problems. | |||||

Lecture notes | Lecture will be only at the blackboard. | |||||

Literature | West, D.: "Introduction to Graph Theory" Diestel, R.: "Graph Theory" Further literature links will be provided in the lecture. | |||||

Prerequisites / Notice | Students are expected to have a mathematical background and should be able to write rigorous proofs. NOTICE: This course unit was previously offered as 252-1408-00L Graphs and Algorithms. | |||||

401-3903-11L | Geometric Integer Programming | W | 6 credits | 2V + 1U | J. Paat | |

Abstract | Integer programming is the task of minimizing a linear function over all the integer points in a polyhedron. This lecture introduces the key concepts of an algorithmic theory for solving such problems. | |||||

Objective | The purpose of the lecture is to provide a geometric treatment of the theory of integer optimization. | |||||

Content | Key topics are: - Lattice theory and the polynomial time solvability of integer optimization problems in fixed dimension. - Structural properties of integer sets that reveal other parameters affecting the complexity of integer problems - Duality theory for integer optimization problems from the vantage point of lattice free sets. | |||||

Lecture notes | not available, blackboard presentation | |||||

Literature | Lecture notes will be provided. Other helpful materials include Bertsimas, Weismantel: Optimization over Integers, 2005 and Schrijver: Theory of linear and integer programming, 1986. | |||||

Prerequisites / Notice | "Mathematical Optimization" (401-3901-00L) | |||||

227-0560-00L | Deep Learning for Autonomous Driving Registration in this class requires the permission of the instructors. Class size will be limited to 80 students. Preference is given to EEIT, INF and RSC students. | W | 6 credits | 3V + 2P | D. Dai, A. Liniger | |

Abstract | Autonomous driving has moved from the realm of science fiction to a very real possibility during the past twenty years, largely due to rapid developments of deep learning approaches, automotive sensors, and microprocessor capacity. This course covers the core techniques required for building a self-driving car, especially the practical use of deep learning through this theme. | |||||

Objective | Students will learn about the fundamental aspects of a self-driving car. They will also learn to use modern automotive sensors and HD navigational maps, and to implement, train and debug their own deep neural networks in order to gain a deep understanding of cutting-edge research in autonomous driving tasks, including perception, localization and control. After attending this course, students will: 1) understand the core technologies of building a self-driving car; 2) have a good overview over the current state of the art in self-driving cars; 3) be able to critically analyze and evaluate current research in this area; 4) be able to implement basic systems for multiple autonomous driving tasks. | |||||

Content | We will focus on teaching the following topics centered on autonomous driving: deep learning, automotive sensors, multimodal driving datasets, road scene perception, ego-vehicle localization, path planning, and control. The course covers the following main areas: I) Foundation a) Fundamentals of a self-driving car b) Fundamentals of deep-learning II) Perception a) Semantic segmentation and lane detection b) Depth estimation with images and sparse LiDAR data c) 3D object detection with images and LiDAR data d) Object tracking and motion prediction III) Localization a) GPS-based and Vision-based Localization b) Visual Odometry and Lidar Odometry IV) Path Planning and Control a) Path planning for autonomous driving b) Motion planning and vehicle control c) Imitation learning and reinforcement learning for self driving cars The exercise projects will involve training complex neural networks and applying them on real-world, multimodal driving datasets. In particular, students should be able to develop systems that deal with the following problems: - Sensor calibration and synchronization to obtain multimodal driving data; - Semantic segmentation and depth estimation with deep neural networks ; - Learning to drive with images and map data directly (a.k.a. end-to-end driving) | |||||

Lecture notes | The lecture slides will be provided as a PDF. | |||||

Prerequisites / Notice | This is an advanced grad-level course. Students must have taken courses on machine learning and computer vision or have acquired equivalent knowledge. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. All practical exercises will require basic knowledge of Python and will use libraries such as PyTorch, scikit-learn and scikit-image. | |||||

227-1034-00L | Computational Vision (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI402 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 credits | 2V + 1U | D. Kiper | |

Abstract | This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision. | |||||

Objective | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||

Content | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||

Literature | Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995. | |||||

Seminar in General Studies | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-3002-00L | Algorithms for Database Systems Number of participants limited to 15. 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. Penna | |

Abstract | Query processing, optimization, stream-based systems, distributed and parallel databases, non-standard databases. | |||||

Objective | Develop an understanding of selected problems of current interest in the area of algorithms for database systems. | |||||

252-4102-00L | Seminar on Randomized Algorithms and Probabilistic Methods 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. Number of participants limited to 24. | W | 2 credits | 2S | A. Steger | |

Abstract | The aim of the seminar is to study papers which bring the students to the forefront of today's research topics. This semester we will study selected papers of the conference Symposium on Discrete Algorithms (SODA18). | |||||

Objective | Read papers from the forefront of today's research; learn how to give a scientific talk. | |||||

Prerequisites / Notice | The seminar is open for both students from mathematics and students from computer science. As prerequisite we require that you passed the course Randomized Algorithms and Probabilistic Methods (or equivalent, if you come from abroad). | |||||

252-5704-00L | Advanced Methods in Computer Graphics 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 | O. Sorkine Hornung | |

Abstract | This seminar covers advanced topics in computer graphics with a focus on the latest research results. Topics include modeling, rendering, visualization, animation, physical simulation, computational photography, and others. | |||||

Objective | The goal is to obtain an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentation and critical analysis skills. | |||||

261-5113-00L | Computational Challenges in Medical Genomics Number of participants limited to 20. | W | 2 credits | 2S | A. Kahles, G. Rätsch | |

Abstract | This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion. | |||||

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 succesfully present the key points of existing research work are the same as 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 from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, 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 amongst the list of selected papers. | |||||

Prerequisites / Notice | Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine. | |||||

263-3712-00L | Seminar on Computational Interaction Number of participants limited to 14. 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. Hilliges | |

Abstract | Computational Interaction focuses on the use of algorithms to enhance the interaction with a computing system. Papers from scientific venues such as CHI, UIST & SIGGRAPH will be examined in-depth. Student present and discuss the papers to extract techniques and insights that can be applied to software & hardware projects. Topics include user modeling, computational design, and input & output. | |||||

Objective | The goal of the seminar is to familiarize students with exciting new research topics in this important area, but also to teach basic scientific writing and oral presentation skills. | |||||

Content | The seminar will have a different structure from regular seminars to encourage more discussion and a deeper learning experience. We will use a case-study format where all students read the same paper each week but fulfill different roles and hence prepare with different viewpoints in mind (e.g. "presenter", "historian", "student", etc). The seminar will cover multiple topics of computational interaction, including: 1) User- and context modeling for UI adaptation Intent modeling, activity and emotion recognition, and user perception. 2) Computational design Design mining, design exploration, UI optimization. 3) Computer supported input Text entry, pointing, gestural input, physiological sensing, eye tracking, and sketching. 4) Computer supported output Information retrieval, fabrication, mixed reality interfaces, haptics, and gaze contingency For each topic, a paper will be chosen that represents the state of the art of research or seminal work that inspired and fostered future work. Student will learn how to incorporate computational methods into system that involve software, hardware, and, very importantly, users. Seminar website: https://ait.ethz.ch/teaching/courses/2020-SS-Seminar-Computational-Interaction/ | |||||

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, 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. | |||||

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. | |||||

263-2100-00L | 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, P. He, M. Rigger, T. Su | |

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 | Papers will be distributed during the first lecture. | |||||

263-2211-00L | 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 has 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: https://safari.ethz.ch/architecture_seminar/ Past course materials, including the synthesis report assignment, can be found in the Fall 2019 website for the course: https://safari.ethz.ch/architecture_seminar/fall2019/doku.php | |||||

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 | Design of Digital Circuits. Students should (1) have done very well in Design of Digital Circuits 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 http://deepcode.ai), academia (e.g., http://plml.ethz.ch) 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. | |||||

263-3840-00L | Hardware Architectures for Machine 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 | G. Alonso, T. Hoefler, C. Zhang | |

Abstract | The seminar covers recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers. | |||||

Objective | The seminar aims at students interested in the system aspects of machine learning, who are willing to bridge the gap across traditional disciplines: machine learning, databases, systems, and computer architecture. | |||||

Content | The seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers. | |||||

Prerequisites / Notice | The seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics. | |||||

263-4651-00L | Current Topics in Cryptography 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 | D. Hofheinz, U. Maurer, K. Paterson | |

Abstract | In this seminar course, students present and discuss a variety of recent research papers in Cryptography. | |||||

Objective | Independent study of scientific literature and assessment of its contributions as well as learning and practicing presentation techniques. | |||||

Content | The course lecturers will provide a list of papers from which students will select. | |||||

Literature | The reading list will be published on the course website. | |||||

Prerequisites / Notice | Ideally, students will have taken the D-INFK Bachelors course “Information Security" or an equivalent course at Bachelors level. Ideally, they will have attended or will attend in parallel the Masters course in "Applied Cryptography”. | |||||

263-5225-00L | Advanced Topics in Machine Learning and Data Science Number of participants limited to 20. 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 | F. Perez Cruz | |

Abstract | In this seminar, recent papers of the machine learning and data science literature are presented and discussed. Possible topics cover statistical models, machine learning algorithms and its applications. | |||||

Objective | The seminar “Advanced Topics in Machine Learning and Data Science” familiarizes students with recent developments in machine learning and data science. Recently published articles, as well as influential papers, have to be presented and critically reviewed. The students will learn how to structure a scientific presentation, which covers the motivation, key ideas and main results of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth for the audience to be able to follow its main conclusion, especially why the article is (or is not) worth attention. 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 machine learning and data science literatures. The topics will vary from year to year but they are centered on methodological issues in machine learning and its application, not only to text or images, but other scientific domains like medicine, climate or physics. | |||||

Literature | The papers will be presented in the first session of the seminar. | |||||

263-5904-00L | Deep Learning for Computer Vision: Seminal Work 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. R. Oswald, Z. Cui | |

Abstract | This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and discuss the papers and gain an understanding of the most influential research in this area - both past and present. | |||||

Objective | The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key contributions to the field of deep learning for vision (including a historical perspective as well as recent work). Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as well as how to give a scientific presentation and lead a discussion on their topic. | |||||

Content | The seminar will start with introductory lectures to provide (1) a compact overview of challenges and relevant machine learning and deep learning research, and (2) a tutorial on critical analysis and presentation of research papers. Each student then chooses one paper from the provided collection to present during the remainder of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants. | |||||

Lecture notes | The selection of research papers will be presented at the beginning of the semester. | |||||

Literature | The course "Machine Learning" is recommended. | |||||

227-0559-00L | Seminar in Deep Reinforcement Learning Number of participants limited to 25. | W | 2 credits | 2S | R. Wattenhofer, O. Richter | |

Abstract | In this seminar participating students present and discuss recent research papers in the area of deep reinforcement learning. The seminar starts with two introductory lessons introducing the basic concepts. Alongside the seminar a programming challenge is posed in which students can take part to improve their grade. | |||||

Objective | Since Google Deepmind presented the Deep Q-Network (DQN) algorithm in 2015 that could play Atari-2600 games at a superhuman level, the field of deep reinforcement learning gained a lot of traction. It sparked media attention with AlphaGo and AlphaZero and is one of the most prominent research areas. Yet many research papers in the area come from one of two sources: Google Deepmind or OpenAI. In this seminar we aim at giving the students an in depth view on the current advances in the area by discussing recent papers as well as discussing current issues and difficulties surrounding deep reinforcement learning. | |||||

Content | Two introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: www.disco.ethz.ch/courses.html | |||||

Lecture notes | Slides of presentations will be made available. | |||||

Literature | OpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers. The paper selection can be found on www.disco.ethz.ch/courses.html. | |||||

Prerequisites / Notice | It is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses. | |||||

227-0559-10L | Seminar in Communication Networks: Learning, Reasoning and Control Does not take place this semester. Number of participants limited to 24. | W | 2 credits | 2S | L. Vanbever, A. Singla | |

Abstract | In this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. This semester the seminar will focus on topics blending networks with machine learning and control theory. | |||||

Objective | The two main goals of this seminar are: 1) learning how to read and review scientific papers; and 2) learning how to present and discuss technical topics with an audience of peers. Students are required to attend the entire seminar, choose a paper to present from a given list, prepare and give a presentation on that topic, and lead the follow-up discussion. To ensure the talks' quality, each student will be mentored by a teaching assistant. In addition to presenting one paper, every student is also required to submit one (short) review for one of the two papers presented every week in-class (12 reviews in total). The students will be evaluated based on their submitted reviews, their presentation, their leadership in animating the discussion for their own paper, and their participation in the discussions of other papers. | |||||

Content | The seminar will start with two introductory lectures in week 1 and week 2. Starting from week 3, participating students will start reviewing, presenting, and discussing research papers. Each week will see two presentations, for a total of 24 papers. The course content will vary from semester to semester. This semester, the seminar will focus on topics blending networks with machine learning and control theory. For details, please see: https://seminar-net.ethz.ch | |||||

Lecture notes | The slides of each presentation will be made available on the website. | |||||

Literature | The paper selection will be made available on the course website: https://seminar-net.ethz.ch | |||||

Prerequisites / Notice | Communication Networks (227-0120-00L) or equivalents. It is expected that students have prior knowledge in machine learning and control theory, for instance by having attended appropriate courses. | |||||

227-0126-00L | Advanced Topics in Networked Embedded Systems | W | 2 credits | 1S | L. Thiele, J. Beutel | |

Abstract | The seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems, internet of things, and sensor networks in various application domains. | |||||

Objective | The goal is to get a deeper understanding on leading edge technologies in the discipline, on classes of applications, and on current as well as future research directions. In addition, participants will improve their presentation, reading and reviewing skills. | |||||

Content | The seminar enables Master students, PhDs and Postdocs to learn about latest breakthroughs in wireless sensor networks, networked embedded systems and devices, and energy-harvesting in several application domains, including environmental monitoring, tracking, smart buildings and control. Participants are requested to actively participate in the organization and preparation of the seminar. In particular, they review all presented papers using a standard scientific reviewing system, they present one of the papers orally and they lead the corresponding discussion session. | |||||

851-0740-00L | Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 1.3.2020 at the latest. | W | 3 credits | 2S | S. Bechtold | |

Abstract | This course introduces students to societal perspectives on the big data revolution. Discussing important contributions from machine learning and data science, the course explores their legal, economic, ethical, and political implications in the past, present, and future. | |||||

Objective | This course is intended both for students of machine learning and data science who want to reflect on the societal implications of their field, and for students from other disciplines who want to explore the societal impact of data sciences. The course will first discuss some of the methodological foundations of machine learning, followed by a discussion of research papers and real-world applications where big data and societal values may clash. Potential topics include the implications of big data for privacy, liability, insurance, health systems, voting, and democratic institutions, as well as the use of predictive algorithms for price discrimination and the criminal justice system. Guest speakers, weekly readings and reaction papers ensure a lively debate among participants from various backgrounds. |