# Suchergebnis: Katalogdaten im Frühjahrssemester 2021

Cyber Security Master | ||||||

Ergänzung | ||||||

Software Engineering | ||||||

Kernfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|

263-2925-00L | Program Analysis for System Security and Reliability | W | 7 KP | 2V + 1U + 3A | M. Vechev | |

Kurzbeschreibung | Security issues in modern systems (blockchains, datacenters, deep learning, etc.) result in billions of losses due to hacks and system downtime. This course introduces fundamental techniques (ranging from automated analysis, machine learning, synthesis, zero-knowledge and their combinations) that can be applied in practice so to build more secure and reliable modern systems. | |||||

Lernziel | * Understand the fundamental techniques used to create modern security and reliability analysis engines that are used worldwide. * Understand how symbolic techniques are combined with machine learning (e.g., deep learning, reinforcement learning) so to create new kinds of learning-based analyzers. * Understand how to quantify and fix security and reliability issues in modern deep learning models. * Understand open research questions from both theoretical and practical perspectives. | |||||

Inhalt | Please see: https://www.sri.inf.ethz.ch/teaching/pass2021 for detailed course content. | |||||

Wahlfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

263-2812-00L | Program Verification Maximale Teilnehmerzahl: 30. | W | 5 KP | 3G + 1A | P. Müller, C. Matheja | |

Kurzbeschreibung | A hands-on introduction to the theory and construction of deductive program verifiers, covering both powerful techniques for formal program reasoning, and a perspective over the tool stack making up modern verification tools. | |||||

Lernziel | Students will earn the necessary skills for designing, developing, and applying deductive verification tools that enable the modular verification of complex software, including features challenging for reasoning such as heap-based mutable data and concurrency. Students will learn both a variety of fundamental reasoning principles, and how these reasoning ideas can be made practical via automatic tools. By the end of the course, students should have a good working understanding and decisions involved with designing and building practical verification tools, including the underlying theory. They will also be able to apply such tools to develop formally-verified programs. | |||||

Inhalt | The course will cover verification techniques and ways to automate them by introducing a verifier for a small core language and then progressively enriching the language with advanced features such as a mutable heap and concurrency. For each language extension, the course will explain the necessary reasoning principles, specification techniques, and tool support. In particular, it will introduce SMT solvers to prove logical formulas, intermediate verification languages to encode verification problems, and source code verifiers to handle feature-rich languages. The course will intermix technical content with hands-on experience. | |||||

Skript | The slides will be available online. | |||||

Literatur | Will be announced in the lecture. | |||||

Voraussetzungen / Besonderes | A basic familiarity with propositional and first-order logic will be assumed. Courses with an emphasis on formal reasoning about programs (such as Formal Methods and Functional Programming) are advantageous background, but are not a requirement. | |||||

263-2815-00L | Automated Software Testing | W | 7 KP | 2V + 1U + 3A | Z. Su | |

Kurzbeschreibung | This course introduces students to classic and modern techniques for the automated testing and analysis of software systems for reliability, security, and performance. It covers both techniques and their applications in various domains (e.g., compilers, databases, theorem provers, operating systems, machine/deep learning, and mobile applications), focusing on the latest, important results. | |||||

Lernziel | * Learn fundamental and practical techniques for software testing and analysis * Understand the challenges, open issues and opportunities across a variety of domains (security/systems/compilers/databases/mobile/AI/education) * Understand how latest automated testing and analysis techniques work * Gain conceptual and practical experience in techniques/tools for reliability, security, and performance * Learn how to perform original and impactful research in this area | |||||

Inhalt | The course will be organized into the following components: (1) classic and modern testing and analysis techniques (coverage metrics, mutation testing, metamorphic testing, combinatorial testing, symbolic execution, fuzzing, static analysis, etc.), (2) latest results on techniques and applications from diverse domains, and (3) open challenges and opportunities. A major component of this course is a class project. All students (individually or two-person teams) are expected to select and complete a course project. Ideally, the project is original research related in a broad sense to automated software testing and analysis. Potential project topics will also be suggested by the teaching staff. Students must select a project and write a one or two pages proposal describing why what the proposed project is interesting and giving a work schedule. Students will also write a final report describing the project and prepare a 20-30 minute presentation at the end of the course. The due dates for the project proposal, final report, and project presentation will be announced. The course will cover results from the Advanced Software Technologies (AST) Lab at ETH as well as notable results elsewhere, providing good opportunities for potential course project topics as well as MSc project/thesis topics. | |||||

Skript | Lecture notes/slides and other lecture materials/handouts will be available online. | |||||

Literatur | Reading material and links to tools will be published on the course website. | |||||

Voraussetzungen / Besonderes | The prerequisites for this course are some programming and algorithmic experience. Background and experience in software engineering, programming languages/compilers, and security (as well as operating systems and databases) can be beneficial. | |||||

Theoretical Computer Science | ||||||

Kernfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

261-5110-00L | Optimization for Data Science | W | 10 KP | 3V + 2U + 4A | B. Gärtner, D. Steurer, N. He | |

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

Lernziel | 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. | |||||

Inhalt | 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, Nesterov's accelerated method, proximal and splitting algorithms, subgradient descent, stochastic gradient descent, variance-reduced methods, 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. | |||||

Voraussetzungen / Besonderes | 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. | |||||

Wahlfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

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

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

263-4400-00L | Advanced Graph Algorithms and Optimization | W | 8 KP | 3V + 1U + 3A | R. Kyng, M. Probst | |

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

Lernziel | 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. | |||||

Inhalt | 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. | |||||

Voraussetzungen / Besonderes | 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. | |||||

272-0302-00L | Approximations- und Online-Algorithmen Findet dieses Semester nicht statt. | W | 5 KP | 2V + 1U + 1A | ||

Kurzbeschreibung | Diese Lerneinheit behandelt approximative Verfahren für schwere Optimierungsprobleme und algorithmische Ansätze zur Lösung von Online-Problemen sowie die Grenzen dieser Ansätze. | |||||

Lernziel | Auf systematische Weise einen Überblick über die verschiedenen Entwurfsmethoden von approximativen Verfahren für schwere Optimierungsprobleme und Online-Probleme zu gewinnen. Methoden kennenlernen, die Grenzen dieser Ansätze aufweisen. | |||||

Inhalt | Approximationsalgorithmen sind einer der erfolgreichsten Ansätze zur Behandlung schwerer Optimierungsprobleme. Dabei untersucht man die sogenannte Approximationsgüte, also das Verhältnis der Kosten einer berechneten Näherungslösung und der Kosten einer (nicht effizient berechenbaren) optimalen Lösung. Bei einem Online-Problem ist nicht die gesamte Eingabe von Anfang an bekannt, sondern sie erscheint stückweise und für jeden Teil der Eingabe muss sofort ein entsprechender Teil der endgültigen Ausgabe produziert werden. Die Güte eines Algorithmus für ein Online-Problem misst man mit der competitive ratio, also dem Verhältnis der Kosten der berechneten Lösung und der Kosten einer optimalen Lösung, wie man sie berechnen könnte, wenn die gesamte Eingabe bekannt wäre. Inhalt dieser Lerneinheit sind - die Klassifizierung von Optimierungsproblemen nach der erreichbaren Approximationsgüte, - systematische Methoden zum Entwurf von Approximationsalgorithmen (z. B. Greedy-Strategien, dynamische Programmierung, LP-Relaxierung), - Methoden zum Nachweis der Nichtapproximierbarkeit, - klassische Online-Probleme wie Paging oder Scheduling-Probleme und Algorithmen zu ihrer Lösung, - randomisierte Online-Algorithmen, - Entwurfs- und Analyseverfahren für Online-Algorithmen, - Grenzen des "competitive ratio"- Modells und Advice-Komplexität als eine Möglichkeit, die Komplexität von Online-Problemen genauer zu messen. | |||||

Literatur | Die Vorlesung orientiert sich teilweise an folgenden Büchern: J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004 D. Komm: An Introduction to Online Computation: Determinism, Randomization, Advice, Springer, 2016 Zusätzliche Literatur: A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998 | |||||

401-3052-10L | Graph Theory | W | 10 KP | 4V + 1U | B. Sudakov | |

Kurzbeschreibung | Basics, trees, Caley's formula, matrix tree theorem, connectivity, theorems of Mader and Menger, Eulerian graphs, Hamilton cycles, theorems of Dirac, Ore, Erdös-Chvatal, matchings, theorems of Hall, König, Tutte, planar graphs, Euler's formula, Kuratowski's theorem, graph colorings, Brooks' theorem, 5-colorings of planar graphs, list colorings, Vizing's theorem, Ramsey theory, Turán's theorem | |||||

Lernziel | 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. | |||||

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

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

Voraussetzungen / Besonderes | Students are expected to have a mathematical background and should be able to write rigorous proofs. | |||||

Visual Computing | ||||||

Kernfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

252-0538-00L | Shape Modeling and Geometry Processing | W | 8 KP | 2V + 1U + 4A | O. Sorkine Hornung | |

Kurzbeschreibung | 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. | |||||

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

Inhalt | 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. | |||||

Skript | Slides and course notes | |||||

Voraussetzungen / Besonderes | 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. | |||||

Wahlfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

252-0526-00L | Statistical Learning Theory | W | 8 KP | 3V + 2U + 2A | J. M. Buhmann, C. Cotrini Jimenez | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | - 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. | |||||

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

Literatur | 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 | |||||

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

252-0570-00L | Game Programming Laboratory Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt. | W | 10 KP | 9P | B. Sumner | |

Kurzbeschreibung | Das Ziel dieses Kurses ist ein vertieftes Verständnis der Technologie und der Programmierung von Computer-Spielen. Die Studierenden entwerfen und entwickeln in kleinen Gruppen ein Computer-Spiel und machen sich so vertraut mit der Kunst des Spiel-Programmierens. | |||||

Lernziel | Das Ziel dieses neuen Kurses ist es, die Studenten mit der Technologie und der Kunst des Programmierens von modernen dreidimensionalen Computerspielen vertraut zu machen. | |||||

Inhalt | Dies ist ein Kurs, der auf die Technologie von modernen dreidimensionalen Computerspielen eingeht. Während des Kurses werden die Studenten in kleinen Gruppen ein Computerspiel entwerfen und entwickeln. Der Schwerpunkt des Kurses wird auf technischen Aspekten der Spielentwicklung wie Rendering, Kinematographie, Interaktion, Physik, Animation und KI liegen. Zusätzlich werden wir aber auch Wert auf kreative Ideen für fortgeschrittenes Gameplay und visuelle Effekte legen. Der Kurs wird als Labor durchgeführt. Zusätzlich zu Vorträgen und Übungen wird der Kurs in einen praktischen, hands-on Ansatz durchgeführt. Wir treffen uns einmal wöchentlich um technische Aspekte zu besprechen und den Fortschritt der Entwicklung zu verfolgen. Für die Enwicklung verwenden wir MonoGames. Dies ist eine Ansammlung von Bibliotheken und Werkzeugen um die Spieleentwicklung zu erleichtern. Die Entwicklung wird zunächst auf dem PC stattfinden, das Spiel wird dann im weiteren Verlauf auf der Xbox One Konsole eingesetzt. Am Ende des Kurses werden die Resultate öffentlich präsentiert. | |||||

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

Voraussetzungen / Besonderes | Die Anzahl der Teilnehmer ist begrenzt. Voraussetzung für die Teilnahme sind: - Gute Programmierkenntnisse (Java, C++, C#, o.ä.) - Erfahrung in Computergrafik: Teilnehmer sollten mindestens die Vorlesung Visual Computing besucht haben. Wir empfehlen auch noch die weiterführenden Kurse Introduction to Computer Graphics, Surface Representations and Geometric Modeling, und Physically-based Simulation in Computer Graphics. | |||||

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

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

252-5706-00L | Mathematical Foundations of Computer Graphics and Vision | W | 5 KP | 2V + 1U + 1A | T. Aydin, A. Djelouah | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

263-3710-00L | Machine Perception Number of participants limited to 200. | W | 8 KP | 3V + 2U + 2A | O. Hilliges, S. Tang | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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 | |||||

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

Voraussetzungen / Besonderes | *** In accordance with the ETH Covid-19 master plan the lecture will be fully virtual. Details on the course website. *** 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 Pytorch, scikit-learn and scikit-image. We will provide introductions to Pytorch 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. | |||||

263-5701-00L | Visualization | W | 5 KP | 2V + 1U + 1A | M. Gross, T. Günther | |

Kurzbeschreibung | This lecture provides an introduction into visualization of scientific and abstract data. | |||||

Lernziel | This lecture provides an introduction into the visualization of scientific and abstract data. The lecture introduces into the two main branches of visualization: scientific visualization and information visualization. The focus is set onto scientific data, demonstrating the usefulness and necessity of computer graphics in other fields than the entertainment industry. The exercises contain theoretical tasks on the mathematical foundations such as numerical integration, differential vector calculus, and flow field analysis, while programming exercises familiarize with the Visualization Tool Kit (VTK). In a course project, the learned methods are applied to visualize one real scientific data set. The provided data sets contain measurements of volcanic eruptions, galaxy simulations, fluid simulations, meteorological cloud simulations and asteroid impact simulations. | |||||

Inhalt | This lecture opens with human cognition basics, and scalar and vector calculus. Afterwards, this is applied to the visualization of air and fluid flows, including geometry-based, topology-based and feature-based methods. Further, the direct and indirect visualization of volume data is discussed. The lecture ends on the viualization of abstract, non-spatial and multi-dimensional data by means of information visualization. | |||||

Voraussetzungen / Besonderes | Fundamentals of differential calculus. Knowledge on numerical mathematics, computer algebra systems, as well as ordinary and partial differential equations is an asset, but not required. | |||||

263-5806-00L | Computational Models of Motion | W | 8 KP | 2V + 2U + 3A | S. Coros, M. Bächer, B. Thomaszewski | |

Kurzbeschreibung | 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 | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

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

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. Please send an email to Dengxin Dai <dai@vision.ee.ethz.ch> about your courses/projects that are related to machine learning, computer vision, and Robotics. | W | 6 KP | 3V + 2P | D. Dai, A. Liniger | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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 Lane Detection 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 ; - 3D object detection and tracking in LiDAR point clouds | |||||

Skript | The lecture slides will be provided as a PDF. | |||||

Voraussetzungen / Besonderes | 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/deadlines.html | W | 6 KP | 2V + 1U | D. Kiper | |

Kurzbeschreibung | 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. | |||||

Lernziel | 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. | |||||

Inhalt | 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. | |||||

Literatur | 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. | |||||

Vertiefungsübergreifende Fächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

263-0007-00L | Advanced Systems Lab Only for master students, otherwise a special permission by the study administration of D-INFK is required. | W | 8 KP | 3V + 2U + 2A | M. Püschel, C. Zhang | |

Kurzbeschreibung | This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning. | |||||

Lernziel | Software performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project. | |||||

Inhalt | The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing. Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. | |||||

Voraussetzungen / Besonderes | Solid knowledge of the C programming language and matrix algebra. | |||||

263-0008-00L | Computational Intelligence LabOnly for master students, otherwise a special permission by the study administration of D-INFK is required. | W | 8 KP | 2V + 2U + 3A | T. Hofmann | |

Kurzbeschreibung | This laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications. | |||||

Lernziel | Students acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and experimental validation. This lab course has a strong focus on practical assignments. Students work in groups of three to four people, to develop solutions to three application problems: 1. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery. For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper. (Disclaimer: The offered projects may be subject to change from year to year.) | |||||

Inhalt | see course description |

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