# Search result: Catalogue data in Spring Semester 2018

CAS in Computer Science | ||||||

Focus Courses and Electives | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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227-0558-00L | Principles of Distributed Computing | W | 6 credits | 2V + 2U + 1A | 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.) | |||||

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, K. A. Martin | |

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. | |||||

252-0312-00L | Ubiquitous Computing | W | 3 credits | 2V | F. Mattern, S. Mayer | |

Abstract | Ubiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact. | |||||

Objective | The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact. | |||||

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 | |||||

252-0355-00L | Object Databases | W | 4 credits | 2V + 1U | A. K. de Spindler | |

Abstract | The course examines the principles and techniques of providing data management in object-oriented programming environments. After introducing the basics of object storage and management, we will cover semantic object models and their implementation. Finally, we discuss advanced data management services such as version models for temporal and engineering databases and for software configuration. | |||||

Objective | The goal of this course is to extend the student's knowledge of database technologies towards object-oriented solutions. Starting with basic principles, students also learn about commercial products and research projects in the domain of object-oriented data management. Apart from getting to know the characteristics of these approaches and the differences between them, the course also discusses what application requirements justify the use of object-oriented databases. Therefore, it educates students to make informed decisions on when to use what database technology. | |||||

Content | The course examines the principles and techniques of providing data management in object-oriented programming environments. It is divided into three parts that cover the road from simple object persistence, to object-oriented database management systems and to advanced data management services. In the first part, object serialisation and object-relational mapping frameworks will be introduced. Using the example of the open-source project db4o, the utilisation, architecture and functionality of a simple object-oriented database is discussed. The second part of the course is dedicated to advanced topics such as industry standards and solutions for object data management as well as storage and index technologies. Additionally, advanced data management services such as version models for temporal and engineering databases as well as for software configuration are discussed. In the third and last part of the course, an object-oriented data model that features a clear separation of typing and classification is presented. Together with the model, its implementation in terms of an object-oriented database management system is discussed also. Finally, an extension of this data model is presented that allows context-aware data to be managed. | |||||

Prerequisites / Notice | Prerequisites: Knowledge about the topics of the lectures "Introduction to Databases" and "Information Systems" is required. | |||||

252-0407-00L | Cryptography Foundations | W | 7 credits | 3V + 2U + 1A | U. Maurer | |

Abstract | Fundamentals and applications of cryptography. Cryptography as a mathematical discipline: reductions, constructive cryptography paradigm, security proofs. The discussed primitives include cryptographic functions, pseudo-randomness, symmetric encryption and authentication, public-key encryption, key agreement, and digital signature schemes. Selected cryptanalytic techniques. | |||||

Objective | The goals are: (1) understand the basic theoretical concepts and scientific thinking in cryptography; (2) understand and apply some core cryptographic techniques and security proof methods; (3) be prepared and motivated to access the scientific literature and attend specialized courses in cryptography. | |||||

Content | See course description. | |||||

Lecture notes | yes. | |||||

Prerequisites / Notice | Familiarity with the basic cryptographic concepts as treated for example in the course "Information Security" is required but can in principle also be acquired in parallel to attending the course. | |||||

252-0526-00L | Statistical Learning Theory | W | 6 credits | 2V + 3P | J. M. Buhmann | |

Abstract | The course covers advanced methods of statistical learning : Statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models. | |||||

Objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed. | |||||

Content | # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Statistical physics models: approaches for large systems approximate optimization, which originate in the statistical physics (free energy minimization applied to spin glasses and other models); sampling methods based on these models | |||||

Lecture notes | A draft of a script will be provided; transparencies of the lectures 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 | Requirements: knowledge of the Machine Learning course basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course. | |||||

252-0538-00L | Shape Modeling and Geometry Processing | W | 5 credits | 2V + 1U + 1A | S. Coros | |

Abstract | This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on polygonal meshes, mesh generation, surface reconstruction, mesh fairing and simplification, 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 digital geometry processing. | |||||

Content | Recent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, 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. Some background in geometry or computational geometry is helpful, but not necessary. | |||||

252-0579-00L | 3D Vision | W | 4 credits | 3G | T. Sattler, M. R. Oswald | |

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. | |||||

252-0820-00L | Case Studies from Practice | W | 4 credits | 2V + 1U | M. Brandis | |

Abstract | The course is designed to provide students with an understanding of "real-life" computer science challenges in business settings and teach them how to address these. | |||||

Objective | By using case studies that are based on actual IT projects, students will learn how to deal with complex, not straightforward problems. It will help them to apply their theoretical Computer Science background in practice and will teach them fundamental principles of IT management and challenges with IT in practice. A particular focus is to make the often imprecise and fuzzy problems in practice accessible to factual analysis and reasoning, and to challenge "common wisdom" and hearsay. | |||||

Content | The course consists of multiple lectures on methods to systematically analyze problems in a business setting and communicate about them as well as IT management and IT economics, presented by the lecturer, and a number of case studies provided by guest lecturers from either IT companies or IT departments of a diverse range of companies. Students will obtain insights into both established and startup companies, small and big, and different industries. Presenting companies have included avaloq, Accenture, AdNovum, Bank Julius Bär, Credit Suisse, Deloitte, HP, Hotelcard, IBM Research, McKinsey & Company, Open Web Technology, SAP Research, Selfnation, SIX Group, Teralytics, 28msec, Zühlke and dormakaba, and Marc Brandis Strategic Consulting. The participating companies in spring 2017 will be announced at course start. | |||||

252-1403-00L | Invitation to Quantum Informatics | W | 3 credits | 2V | S. Wolf | |

Abstract | Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseudo-telepathy", quantum cryptography, as well as the main concepts of quantum information theory. | |||||

Objective | It is the goal of this course to get familiar with the most important notions that are of importance for the connection between Information and Physics. The formalism of Quantum Physics will be motivated and derived, and the use of these laws for information processing will be understood. In particular, the important algorithms of Grover as well as Shor will be studied and analyzed. | |||||

Content | According to Landauer, "information is physical". In quantum information, one is interested in the consequences and the possibilites offered by the laws of quantum physics for information processing. Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseude-telepathy", quantum cryptography, as well as the main concepts of quantum information theory. | |||||

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 | W | 4 credits | 2V + 1U | T. Hofmann, M. Ciaramita | |

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 | 4 credits | 2V + 1U | 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. | |||||

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

Abstract | This course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science. | |||||

Objective | Understanding the theoretical and practical aspects of relevant optimization methods used in data science. Learning general paradigms to deal with optimization problems arising in data science. | |||||

Content | This course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science. In the first part of the course, we will discuss how classical first and second order methods such as gradient descent and Newton's method can be adapated to scale to large datasets, in theory and in practice. We also cover some new algorithms and paradigms that have been developed specifically in the context of data science. The emphasis is not so much on the application of these methods (many of which are covered in other courses), but on understanding and analyzing the methods themselves. 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. | |||||

263-2300-00L | How To Write Fast Numerical Code Does not take place this semester. Number of participants limited to 84. Prerequisite: Master student, solid C programming skills. | W | 6 credits | 3V + 2U | M. Püschel | |

Abstract | This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning. | |||||

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

Content | 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 software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research. Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning. | |||||

263-2812-00L | Program Verification Number of participants limited to 30. | W | 5 credits | 2V + 1U + 1A | A. J. Summers | |

Abstract | A hands-on introduction to the theory and construction of deductive software verifiers, covering both cutting-edge methodologies for formal program reasoning, and a perspective over the broad tool stacks making up modern verification tools. | |||||

Objective | Students will earn the necessary skills for designing and developing deductive verification tools which can be applied to modularly analyse 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. Students will be gain practical experience with reasoning tools at various levels of abstraction, from SAT and SMT solvers at the lowest level, up through intermediate verification languages and tools, to verifiers which target front-end code in executable languages. By the end of the course, students should have a good working understanding and experience of the issues and decisions involved with designing and building practical verification tools, and the theoretical techniques which underpin them. | |||||

Content | The course will be organized around building up a "tool stack", starting at the lowest-level with background on SAT and SMT solving techniques, and working upwards through tools at progressively-higher levels of abstraction. The notion of intermediate verification languages will be explored, and the Boogie (Microsoft Research) and Viper (ETH) languages will be used in depth to tackle increasingly ambitious verification tasks. The course will intermix technical content with hands-on experience; at each level of abstraction, we will build small tools on top which can tackle specific program correctness problems, starting from simple puzzle solvers (Soduko) at the SAT level, and working upwards to full functional correctness of application-level code. This practical work will include three mini-projects (each worth 10% of the final grade) spread throughout the course, which count towards the final grade. An oral examination (worth 70% of the final grade) will cover the technical content covered. | |||||

Lecture notes | Slides and other materials will be available online. | |||||

Literature | Background reading material and links to tools will be published on the course website. | |||||

Prerequisites / Notice | Some programming experience is essential, as the course contains several practical assignments. 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. | |||||

263-2925-00L | Program Analysis for System Security and Reliability | W | 5 credits | 2V + 1U + 1A | M. Vechev | |

Abstract | The course introduces modern analysis and synthesis techniques (both, deterministic and probabilistic) and shows how to apply these methods to build reliable and secure systems spanning the domains of blockchain, computer networks and deep learning. | |||||

Objective | * Understand how classic analysis and synthesis techniques work, including discrete and probabilistic methods. * Understand how to apply the methods to generate attacks and create defenses against applications in blockchain, computer networks and deep learning. * Understand the state-of-the-art in the area as well as future trends. | |||||

Content | The course will illustrate how the methods can be used to create more secure and reliable systems across four application domains: Part I: Analysis and Synthesis for Computer Networks: 1. Analysis: Datalog, Batfish 2. Synthesis: CEGIS, SyNET (http://synet.ethz.ch) 3. Probabilistic: (PSI: http://psisolver.org), its applications to networks (Bayonet) Part II: Blockchain security 1. Introduction to space and tools. 2. Automated symbolic reasoning. 3. Applications: verification of smart contracts (http://www.securify.ch) Part III: Security and Robustness of Deep Learning: 1. Basics: affine transforms, activation functions 2. Attacks: gradient based method to adversarial generation 3. Defenses: affine domains, AI2 (http://ai2.ethz.ch) Part IV: Probabilistic Security: 1. Enforcement: PSI + Spire. 2. Graphical models: CRFs, Structured SVM, Pseudo-likelihood. 3. Practical statistical de-obfuscation: DeGuard: http://apk-deguard.com, JSNice: http://jsnice.org, and more. To gain a deeper understanding, the course will involve a hands-on programming project. | |||||

263-3501-00L | Advanced Computer Networks | W | 5 credits | 2V + 2U | A. Singla, P. M. Stüdi | |

Abstract | This course covers a set of advanced topics in computer networks. The focus is on principles, architectures, and protocols used in modern networked systems, such as the Internet and data center networks. | |||||

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, and protocols used in modern networked systems. Topics include data center network topologies, software defined networking, network function virtualization, flow control and congestion control in data centers, end-point optimizations, and server virtualization. | |||||

263-3710-00L | Machine Perception Students, who have already taken 263-3700-00 User Interface Engineering are not allowed to register for this course! | W | 5 credits | 2V + 1U + 1A | O. Hilliges | |

Abstract | Recent developments in neural network (aka “deep learning”) have drastically advanced the performance of machine perception systems in a variety of areas including drones, self-driving cars and intelligent UIs. This course is a deep dive into details of the deep learning algorithms and architectures for 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 motion. 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 (e.g. backpropagation), practical engineering aspects as well as advanced deep learning algorithms including generative models. The course covers the following main areas: I) Machine-learning algorithms for input recognition, computer vision and image classification (human pose, object detection, gestures, etc.) II) Deep-learning models for the analysis of time-series data (temporal sequences of motion) III) Learning of generative models for synthesis and prediction of human activity. Specific topics include: • Deep learning basics: ○ Neural Networks and training (i.e., backpropagation) ○ Feedforward Networks ○ Recurrent Neural Networks • Deep Learning techniques user input recognition: ○ Convolutional Neural Networks for classification ○ Fully Convolutional architectures for dense per-pixel tasks (i.e., segmentation) ○ LSTMs & related for time series analysis ○ Generative Models (GANs, Variational Autoencoders) • 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 is not meant as extensive tutorial of how to train deep networks with Tensorflow.. Please take note of the following conditions: 1) The number of participants is limited to 100 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: * "Machine Learning" * "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. | |||||

263-4310-00L | Linear Algebra Methods in Combinatorics | W | 5 credits | 2V + 2U | P. Penna | |

Abstract | This course describes the linear algebra bound technique also called dimension argument. To learn the technique we discuss several examples in combinatorics, geometry, and computer science. Besides this technique, the course aims at showing the mathematical elegance of certain proofs and the simplicity of the statements. | |||||

Objective | Becoming familiar with the method and being able to apply it to problems similar to those encountered during the course. | |||||

Content | This course is (essentially) about one single technique called the "linear algebra bound" (also known as "dimension argument"). We shall see several examples in combinatorics, geometry, and computer science and learn the technique throughout these examples. Towards the end of the course, we shall see the power of this method in proving rather amazing results (e.g., a circuit complexity lower bound, explicit constructions of Ramsey graphs, and a famous conjecture in geometry disproved). The course also aims at illustrating the main ideas behind the proofs and how the various problems are in fact connected to each other. | |||||

Lecture notes | Lecture notes of each single lecture will be made available (shortly after the lecture itself). | |||||

Literature | Most of the material of the course is covered by the following book: 1. Linear algebra methods in combinatorics, by L. Babai and P. Frankl, Department of Computer Science, University of Chicago, preliminary version, 1992. Some parts are also taken from 2. Extremal Combinatorics (with Applications in Computer Science), by Stasys Jukna, Springer-Verlag 2001. |

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