Suchergebnis: Katalogdaten im Frühjahrssemester 2018
Data Science Master | ||||||
Kernfächer | ||||||
Datenanalyse | ||||||
Information and Learning | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
227-0434-10L | Mathematics of Information | W | 8 KP | 3V + 2U + 2A | H. Bölcskei | |
Kurzbeschreibung | The class focuses on fundamental mathematical aspects of data sciences: Information theory (lossless and lossy compression), sampling theory, compressed sensing, dimensionality reduction (Johnson-Lindenstrauss Lemma), randomized algorithms for large-scale numerical linear algebra, approximation theory, neural networks as function approximators, mathematical foundations of deep learning. | |||||
Lernziel | After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the most commonly used mathematical theories in data science. Students will also have to carry out a research project, either individually or in groups, with presentations at the end of the semester. | |||||
Inhalt | 1. Information theory: Entropy, mutual information, lossy compression, rate-distortion theory, lossless compression, arithmetic coding, Lempel-Ziv compression 2. Signal representations: Frames in finite-dimensional spaces, frames in Hilbert spaces, wavelets, Gabor expansions 3. Sampling theorems: The sampling theorem as a frame expansion, irregular sampling, multi-band sampling, density theorems, spectrum-blind sampling 4. Sparsity and compressed sensing: Uncertainty principles, recovery algorithms, Lasso, matching pursuits, compressed sensing, non-linear approximation, best k-term approximation, super-resolution 5. High-dimensional data and dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma, sketching 6. Randomized algorithms for large-scale numerical linear algebra: Large-scale matrix computations, randomized algorithms for approximate matrix factorizations, matrix sketching, fast algorithms for large-scale FFTs 7. Mathematics of (deep) neural networks: Universal function approximation with single-and multi-layer networks, fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, geometry of decision surfaces, convolutional neural networks, scattering networks | |||||
Skript | Detailed lecture notes will be provided as we go along. | |||||
Voraussetzungen / Besonderes | This course is aimed at students with a background in basic linear algebra, analysis, and probability. We will, however, review required mathematical basics throughout the semester in the exercise sessions. | |||||
Statistics | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
401-3632-00L | Computational Statistics | W | 10 KP | 3V + 2U | M. H. Maathuis | |
Kurzbeschreibung | Computational Statistics deals with modern statistical methods of data analysis (aka "data science") for prediction and inference. The course provides an overview of existing methods. The course is hands-on, and methods are applied using the statistical programming language R. | |||||
Lernziel | In this class, 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. | |||||
Voraussetzungen / Besonderes | At least one semester of (basic) probability and statistics. Programming experience is helpful but not required. | |||||
Datenmanagement und Datenverarbeitung | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
261-5110-00L | Optimization for Data Science | W | 8 KP | 3V + 2U + 2A | B. Gärtner, D. Steurer | |
Kurzbeschreibung | This course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
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. | |||||
Wählbare Kernfächer | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
151-0566-00L | Recursive Estimation | W | 4 KP | 2V + 1U | R. D'Andrea | |
Kurzbeschreibung | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||
Lernziel | Learn the basic recursive estimation methods and their underlying principles. | |||||
Inhalt | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | |||||
Skript | Lecture notes available on course website: Link | |||||
Voraussetzungen / Besonderes | Requirements: Introductory probability theory and matrix-vector algebra. | |||||
227-0150-00L | Energy-Efficient Parallel Computing Systems for Data Analytics Previously called "Advanced System-on-chip Design: Integrated Parallel Computing Architectures" | W | 6 KP | 4G | L. Benini | |
Kurzbeschreibung | Advanced Parallel Computing Architectures and related design issues. It will cover multi-cores, many-cores, vector engines, GP-GPUs, application-specific processors and heterogeneous compute accelerators. Focus on integrated architectures for data analytics applications. Special emphasis given to energy-efficiency issues and hardware-software design for power and energy minimizazion. | |||||
Lernziel | Give in-depth understanding of the links and dependencies between architectures and their energy-efficient implementation and to get a comprehensive exposure to state-of-the-art computing platforma for data anlytics applications. Practical experience will also be gained through practical exercises and mini-projects (hardware and software) assigned on specific topics. | |||||
Inhalt | The course will cover advanced parallel computing architectures architectures, with an in-depth view on design challenges related to advanced silicon technology and state-of-the-art system integration options (nanometer silicon technology, novel storage devices, three-dimensional integration, advanced system packaging). The emphasis will be on programmable parallel architectures, namely, multi and many- cores, GPUs, vector accelerators, application-specific processors, heterogeneous platforms, and the complex design choices required to achieve scalability and energy proportionality. The course will will also delve into system design, touching on hardware-software tradeoffs and full-system analysis and optimization taking into account non-functional constraints and quality metrics, such as power consumption, thermal dissipation, reliability and variability. The application focus will be on emerging data analytics both in the cloud at at the edges (near-sensor analytics). | |||||
Skript | Slides will be provided to accompany lectures. Pointers to scientific literature will be given. Exercise scripts and tutorials will be provided. | |||||
Literatur | D. Patterson, J. Hennessy, Computer Architecture, Fifth Edition: A Quantitative Approach (The Morgan Kaufmann Series in Computer Architecture and Design), 2011. D. Patterson, J. Hennessy, Computer Organization and Design, Fifth Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design), 2013. | |||||
Voraussetzungen / Besonderes | Knowledge of digital design at the level of "Design of Digital Circuits SS12" is required. Knowledge of basic VLSI design at the level of "VLSI I: Architectures of VLSI Circuits" is required | |||||
227-0224-00L | Stochastic Systems | W | 4 KP | 2V + 1U | F. Herzog | |
Kurzbeschreibung | Probability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering. | |||||
Lernziel | Stochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance. | |||||
Inhalt | - Stochastic processes - Stochastic calculus (Ito) - Stochastic differential equations - Discrete time stochastic difference equations - Stochastic processes AR, MA, ARMA, ARMAX, GARCH - Kalman filter - Stochastic optimal control - Applications in finance and engineering | |||||
Skript | H. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts | |||||
227-0420-00L | Information Theory II Findet dieses Semester nicht statt. | W | 6 KP | 2V + 2U | A. Lapidoth | |
Kurzbeschreibung | This course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory. | |||||
Lernziel | The course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory. | |||||
Inhalt | Differential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem. | |||||
Skript | n/a | |||||
Literatur | T.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006 | |||||
227-0558-00L | Principles of Distributed Computing | W | 6 KP | 2V + 2U + 1A | R. Wattenhofer, M. Ghaffari | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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 | |||||
Skript | Available. Our course script is used at dozens of other universities around the world. | |||||
Literatur | 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 | |||||
Voraussetzungen / Besonderes | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||
252-0211-00L | Information Security | W | 8 KP | 4V + 3U | D. Basin, S. Capkun | |
Kurzbeschreibung | This course provides an introduction to Information Security. The focus is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice. | |||||
Lernziel | Master fundamental concepts in Information Security and their application to system building. (See objectives listed below for more details). | |||||
Inhalt | 1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design, Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility | |||||
252-0526-00L | Statistical Learning Theory | W | 6 KP | 2V + 3P | J. M. Buhmann | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | # 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 | |||||
Skript | A draft of a script will be provided; transparencies of the lectures 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 | 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 KP | 2V + 1U + 1A | S. Coros | |
Kurzbeschreibung | 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. | |||||
Lernziel | The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing. | |||||
Inhalt | 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. | |||||
Skript | Slides and course notes | |||||
Voraussetzungen / Besonderes | 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 KP | 3G | T. Sattler, M. R. Oswald | |
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. | |||||
252-3005-00L | Natural Language Understanding | W | 4 KP | 2V + 1U | T. Hofmann, M. Ciaramita | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Literatur | 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. | |||||
261-5130-00L | Research in Data Science | W | 6 KP | 13A | Professor/innen | |
Kurzbeschreibung | Independent work under the supervision of a core data science professor. | |||||
Lernziel | Independent work under the supervision of a core data science professor. An approval of the director of studies is required for a non-core DS professor. | |||||
Inhalt | Project done under supervision of an approved professor. | |||||
Voraussetzungen / Besonderes | Only students who have passed at least one core course in Data Management and Processing, and one core course in Data Analysis can start with a research project. A project description must be submitted at the start of the project to the studies administration. | |||||
263-0008-00L | Computational Intelligence Lab Only for master students, otherwise a special permission by the study administration of D-INFK is required. | W | 8 KP | 2V + 2U + 1A | 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 two to three 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 | |||||
263-2925-00L | Program Analysis for System Security and Reliability | W | 5 KP | 2V + 1U + 1A | M. Vechev | |
Kurzbeschreibung | 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. | |||||
Lernziel | * 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. | |||||
Inhalt | 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 (Link) 3. Probabilistic: (PSI: Link), 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 (Link) 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 (Link) Part IV: Probabilistic Security: 1. Enforcement: PSI + Spire. 2. Graphical models: CRFs, Structured SVM, Pseudo-likelihood. 3. Practical statistical de-obfuscation: DeGuard: Link, JSNice: Link, and more. To gain a deeper understanding, the course will involve a hands-on programming project. | |||||
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 KP | 2V + 1U + 1A | O. Hilliges | |
Kurzbeschreibung | 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. | |||||
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 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. | |||||
Inhalt | 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 | |||||
Literatur | Deep Learning Book by Ian Goodfellow and Yoshua Bengio | |||||
Voraussetzungen / Besonderes | 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. | |||||
401-0674-00L | Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics. | W | 8 KP | 4V + 2U + 1A | R. Hiptmair | |
Kurzbeschreibung | Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a finite element library. | |||||
Lernziel | Main skills to be acquired in this course: * Ability to implement advanced numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages. | |||||
Inhalt | 1 Case Study: A Two-point Boundary Value Problem 1.1 Introduction 1.2 A model problem 1.3 Variational approach 1.4 Simplified model 1.5 Discretization 1.5.1 Galerkin discretization 1.5.2 Collocation [optional] 1.5.3 Finite differences 1.6 Convergence 2 Second-order Scalar Elliptic Boundary Value Problems 2.1 Equilibrium models 2.1.1 Taut membrane 2.1.2 Electrostatic fields 2.1.3 Quadratic minimization problems 2.2 Sobolev spaces 2.3 Variational formulations 2.4 Equilibrium models: Boundary value problems 3 Finite Element Methods (FEM) 3.1 Galerkin discretization 3.2 Case study: Triangular linear FEM in two dimensions 3.3 Building blocks of general FEM 3.4 Lagrangian FEM 3.4.1 Simplicial Lagrangian FEM 3.4.2 Tensor-product Lagrangian FEM 3.5 Implementation of FEM in C++ 3.5.1 Mesh file format (Gmsh) 3.5.2 Mesh data structures (DUNE) 3.5.3 Assembly 3.5.4 Local computations and quadrature 3.5.5 Incorporation of essential boundary conditions 3.6 Parametric finite elements 3.6.1 Affine equivalence 3.6.2 Example: Quadrilaterial Lagrangian finite elements 3.6.3 Transformation techniques 3.6.4 Boundary approximation 3.7 Linearization [optional] 4 Finite Differences (FD) and Finite Volume Methods (FV) [optional] 4.1 Finite differences 4.2 Finite volume methods (FVM) 5 Convergence and Accuracy 5.1 Galerkin error estimates 5.2 Empirical Convergence of FEM 5.3 Finite element error estimates 5.4 Elliptic regularity theory 5.5 Variational crimes 5.6 Duality techniques [optional] 5.7 Discrete maximum principle [optional] 6 2nd-Order Linear Evolution Problems 6.1 Parabolic initial-boundary value problems 6.1.1 Heat equation 6.1.2 Spatial variational formulation 6.1.3 Method of lines 6.1.4 Timestepping 6.1.5 Convergence 6.2 Wave equations [optional] 6.2.1 Vibrating membrane 6.2.2 Wave propagation 6.2.3 Method of lines 6.2.4 Timestepping 6.2.5 CFL-condition 7 Convection-Diffusion Problems 7.1 Heat conduction in a fluid 7.1.1 Modelling fluid flow 7.1.2 Heat convection and diffusion 7.1.3 Incompressible fluids 7.1.4 Transient heat conduction 7.2 Stationary convection-diffusion problems 7.2.1 Singular perturbation 7.2.2 Upwinding 7.3 Transient convection-diffusion BVP 7.3.1 Method of lines 7.3.2 Transport equation 7.3.3 Lagrangian split-step method 7.3.4 Semi-Lagrangian method 8 Numerical Methods for Conservation Laws 8.1 Conservation laws: Examples 8.2 Scalar conservation laws in 1D 8.3 Conservative finite volume discretization 8.3.1 Semi-discrete conservation form 8.3.2 Discrete conservation property 8.3.3 Numerical flux functions 8.3.4 Montone schemes 8.4 Timestepping 8.4.1 Linear stability 8.4.2 CFL-condition 8.4.3 Convergence 8.5 Higher order conservative schemes [optional] 8.5.1 Slope limiting 8.5.2 MUSCL scheme 8.6. FV-schemes for systems of conservation laws [optional] | |||||
Skript | Lecture documents and classroom notes will be made available to the audience as PDF. | |||||
Literatur | Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course. | |||||
Voraussetzungen / Besonderes | Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks. | |||||
401-3052-05L | Graph Theory | W | 5 KP | 2V + 1U | B. Sudakov | |
Kurzbeschreibung | Basic notions, trees, spanning trees, Caley's formula, vertex and edge connectivity, blocks, 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 | |||||
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 | NOTICE: This course unit was previously offered as 252-1408-00L Graphs and Algorithms. | |||||
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. | |||||
401-3602-00L | Applied Stochastic Processes Findet dieses Semester nicht statt. | W | 8 KP | 3V + 1U | keine Angaben | |
Kurzbeschreibung | Poisson-Prozesse; Erneuerungsprozesse; Markovketten in diskreter und in stetiger Zeit; einige Beispiele und Anwendungen. | |||||
Lernziel | Stochastische Prozesse dienen zur Beschreibung der Entwicklung von Systemen, die sich in einer zufälligen Weise entwickeln. In dieser Vorlesung bezieht sich die Entwicklung auf einen skalaren Parameter, der als Zeit interpretiert wird, so dass wir die zeitliche Entwicklung des Systems studieren. Die Vorlesung präsentiert mehrere Klassen von stochastischen Prozessen, untersucht ihre Eigenschaften und ihr Verhalten und zeigt anhand von einigen Beispielen, wie diese Prozesse eingesetzt werden können. Die Hauptbetonung liegt auf der Theorie; "applied" ist also im Sinne von "applicable" zu verstehen. | |||||
Literatur | R. N. Bhattacharya and E. C. Waymire, "Stochastic Processes with Applications", SIAM (2009), available online: Link R. Durrett, "Essentials of Stochastic Processes", Springer (2012), available online: Link M. Lefebvre, "Applied Stochastic Processes", Springer (2007), available online: Link S. I. Resnick, "Adventures in Stochastic Processes", Birkhäuser (2005) | |||||
Voraussetzungen / Besonderes | Prerequisites are familiarity with (measure-theoretic) probability theory as it is treated in the course "Probability Theory" (401-3601-00L). | |||||
401-3622-00L | Regression | W | 8 KP | 4G | P. L. Bühlmann | |
Kurzbeschreibung | In der Regression wird die Abhängigkeit einer zufälligen Response-Variablen von anderen Variablen untersucht. Wir betrachten die Theorie der linearen Regression mit einer oder mehreren Ko-Variablen, hoch-dimensionale lineare Modelle, nicht-lineare Modelle und verallgemeinerte lineare Modelle, Robuste Methoden, Modellwahl und nicht-parametrische Modelle. | |||||
Lernziel | Einführung in Theorie und Praxis eines umfassenden und vielbenutzten Teilgebiets der Statistik, unter Berücksichtigung neuerer Entwicklungen. | |||||
Inhalt | In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression. Durchrechnung und Diskussion von Anwendungsbeispielen. | |||||
Skript | Vorlesungsskript | |||||
Voraussetzungen / Besonderes | Credits cannot be recognised for both courses 401-3622-00L Regression and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden). | |||||
401-4632-15L | Causality | W | 4 KP | 2G | N. Meinshausen | |
Kurzbeschreibung | In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference. | |||||
Lernziel | After this course, you should be able to - understand the language and concepts of causal inference - know the assumptions under which one can infer causal relations from observational and/or interventional data - describe and apply different methods for causal structure learning - given data and a causal structure, derive causal effects and predictions of interventional experiments | |||||
Voraussetzungen / Besonderes | Prerequisites: basic knowledge of probability theory and regression | |||||
401-4904-00L | Combinatorial Optimization | W | 6 KP | 2V + 1U | R. Zenklusen | |
Kurzbeschreibung | Combinatorial Optimization deals with efficiently finding a provably strong solution among a finite set of options. This course discusses key combinatorial structures and techniques to design efficient algorithms for combinatorial optimization problems. We put a strong emphasis on polyhedral methods, which proved to be a powerful and unifying tool throughout combinatorial optimization. | |||||
Lernziel | The goal of this lecture is to get a thorough understanding of various modern combinatorial optimization techniques with an emphasis on polyhedral approaches. Students will learn a general toolbox to tackle a wide range of combinatorial optimization problems. | |||||
Inhalt | Key topics include: - Polyhedral descriptions; - Combinatorial uncrossing; - Ellipsoid method; - Equivalence between separation and optimization; - Design of efficient approximation algorithms for hard problems. | |||||
Skript | Lecture notes will be available online. | |||||
Literatur | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 5th edition, Springer, 2012. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency, Springer, 2003. This work has 3 volumes. | |||||
Voraussetzungen / Besonderes | Prior exposure to Linear Programming can greatly help the understanding of the material. We therefore recommend that students interested in Combinatorial Optimization get familiarized with Linear Programming before taking this lecture. | |||||
401-6102-00L | Multivariate Statistics Findet dieses Semester nicht statt. | W | 4 KP | 2G | keine Angaben | |
Kurzbeschreibung | Multivariate Statistics deals with joint distributions of several random variables. This course introduces the basic concepts and provides an overview over classical and modern methods of multivariate statistics. We will consider the theory behind the methods as well as their applications. | |||||
Lernziel | After the course, you should be able to: - describe the various methods and the concepts and theory behind them - identify adequate methods for a given statistical problem - use the statistical software "R" to efficiently apply these methods - interpret the output of these methods | |||||
Inhalt | Visualization / Principal component analysis / Multidimensional scaling / The multivariate Normal distribution / Factor analysis / Supervised learning / Cluster analysis | |||||
Skript | None | |||||
Literatur | The course will be based on class notes and books that are available electronically via the ETH library. | |||||
Voraussetzungen / Besonderes | Target audience: This course is the more theoretical version of "Applied Multivariate Statistics" (401-0102-00L) and is targeted at students with a math background. Prerequisite: A basic course in probability and statistics. Note: The courses 401-0102-00L and 401-6102-00L are mutually exclusive. You may register for at most one of these two course units. | |||||
701-0104-00L | Statistical Modelling of Spatial Data | W | 3 KP | 2G | A. J. Papritz | |
Kurzbeschreibung | In environmental sciences one often deals with spatial data. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such analyses. | |||||
Lernziel | The course will provide an overview of the basic concepts and stochastic models that are used to model spatial data. In addition, participants will learn a number of geostatistical techniques and acquire familiarity with R software that is useful for analyzing spatial data. | |||||
Inhalt | After an introductory discussion of the types of problems and the kind of data that arise in environmental research, an introduction into linear geostatistics (models: stationary and intrinsic random processes, modelling large-scale spatial patterns by linear regression, modelling autocorrelation by variogram; kriging: mean square prediction of spatial data) will be taught. The lectures will be complemented by data analyses that the participants have to do themselves. | |||||
Skript | Lecture material, descriptions of the problems for the data analyses and worked out solutions to them will be provided. The course material is available from the Moodle repository Link . | |||||
Literatur | P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer. Bivand, R. S., Pebesma, E. J. & Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R. Springer. | |||||
Voraussetzungen / Besonderes | Familiarity with linear regression analysis (e.g. equivalent to the first part of the course 401-0649-00L Applied Statistical Regression) and with the software R (e.g. 401-6215-00L Using R for Data Analysis and Graphics (Part I), 401-6217-00L Using R for Data Analysis and Graphics (Part II)) are required for attending the course. Course material in English will be provided and the course will be taught in English if participants are not sufficiently fluent in German. | |||||
Interdisziplinäre Wahlfächer | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 KP | 4G | K. W. Axhausen | |
Kurzbeschreibung | Comprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models. | |||||
Lernziel | Enabling the student to understand and apply the various measurement approaches and models of modelling travel behaviour. | |||||
Inhalt | Behavioral model and measurement; travel diary, design process, hypothetical markets, discrete choice model, parameter estimation, pattern of travel behaviour, market segments, simulation, advanced discrete choice models | |||||
Skript | Various papers and notes are distributed during the course. | |||||
Voraussetzungen / Besonderes | Requirement: Transport I | |||||
103-0228-00L | Multimedia Cartography Voraussetzung: Erfolgreicher Abschluss der Lerneinheit Cartography III (103-0227-00L). | W | 4 KP | 3G | H.‑R. Bär, R. Sieber | |
Kurzbeschreibung | Focus of this course is on the realization of an atlas project in a small team. During the first part of the course, the necessary organizational, creative and technological basics will be provided. At the end of the course, the interactive atlas projects will be presented by the team members. | |||||
Lernziel | The goal of this course is to provide the students the theoretical background, knowledge and practical skills necessary to plan, design and create an interactive Web atlas based on modern Web technologies. | |||||
Inhalt | This course will cover the following topics: - Web map design - Project management - Graphical user interfaces in Web atlases - Interactions in map and atlas applications - Web standards - Programming interactive Web applications - Use of software libraries - Cartographic Web services - Code repository - Copyright and the Internet | |||||
Skript | Lecture notes and additional material are available on Moodle. | |||||
Literatur | - Cartwright, William; Peterson, Michael P. and Georg Gartner (2007); Multimedia Cartography, Springer, Heidelberg | |||||
Voraussetzungen / Besonderes | Prerequisites: Successful completion of Cartography III (103-0227-00L). Previous knowledge in Web programming. The students are expected to - present their work in progress on a regular basis - present their atlas project at the end of the course - keep records of all the work done - document all individual contributions to the project | |||||
103-0247-00L | Mobile GIS and Location-Based Services | W | 5 KP | 4G | P. Kiefer | |
Kurzbeschreibung | The course introduces students to the theoretical and technological background of mobile geographic information systems and location-based services. In lab sessions students acquire competences in mobile GIS design and implementation. | |||||
Lernziel | Students will - learn about the implications of mobility on GIS - get a detailed overview on research fields related to mobile GIS - get an overview on current mobile GIS and LBS technology, and learn how to assess new technologies in this fast-moving field - achieve an integrated view of Geospatial Web Services and mobile GIS - acquire competences in mobile GIS design and implementation | |||||
Inhalt | - LBS and mobile GIS: architectures, market, applications, and application development - Development for Android - Mobile decision-making, context, personalization, and privacy - Mobile human computer interaction and user interfaces - Mobile behavior interpretation | |||||
Voraussetzungen / Besonderes | Elementary programming skills (Java) | |||||
103-0255-01L | Geodatenanalyse | W | 2 KP | 2G | D. Jonietz | |
Kurzbeschreibung | Die Lehrveranstaltung behandelt weiterführende Methoden der Geodatenanalyse. | |||||
Lernziel | - Verstehen der theoretischen Grundlagen räumlicher Analyseverfahren. - Verstehen und Anwenden von Methoden zur raumbezogenen Datenanalyse. - Erkennen häufiger Fehlerquellen bei der Geodatenanalyse. - Vertiefende praktische Kenntnisse in der Anwendung entsprechender GIS-Tools. | |||||
Inhalt | In der Lehrveranstaltung werden weiterführende Methoden räumlicher Analyseverfahren theoretisch behandelt sowie anhand von Übungsaufgaben angewendet. | |||||
Skript | kein Skript. | |||||
Literatur | Eine Literaturliste wird in der Lehrveranstaltung zur Verfügung gestellt. | |||||
Voraussetzungen / Besonderes | Voraussetzungen: Basiswissen im Bereich der Geoinformationstechnologien und der Verwendung von Geoinformationssystemen entsprechend den Vorlesungen GIS I und GIS II im Bachelor-Studiengang Geomatik und Planung. | |||||
227-0945-10L | Cell and Molecular Biology for Engineers II This course is part II of a two-semester course. Knowledge of part I is required. | W | 3 KP | 2G | C. Frei | |
Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||
Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||
Inhalt | Lectures will include the following topics: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells. In addition, three journal clubs will be held, where one/two publictions will be discussed. For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded, and count as 25% for the final grade. | |||||
Skript | Scripts of all lectures will be available. | |||||
Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter. | |||||
261-5111-00L | Asset Management: Advanced Investments (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC207 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 3 KP | 2V | Uni-Dozierende | |
Kurzbeschreibung | Comprehension and application of advanced portfolio theory | |||||
Lernziel | Comprehension and application of advanced portfolio theory | |||||
Inhalt | The theoretical part of the lecture consists of the topics listed below. - Standard Markowitz Model and Extensions MV Optimization, MV with Liabilities and CAPM. - The Crux with MV Resampling, regression, Black-Litterman, Bayesian, shrinkage, constrained and robust optimization. - Downside and Coherent Risk Measures Definition of risk measures, MV optimization under VaR and ES constraints. - Risk Budgeting Equal risk contribution, most diversified portfolio and other concentration indices - Regime Switching and Asset Allocation An introduction to regime switching models and its intuition. - Strategic Asset Allocation Introducing a continuous-time framework, solving the HJB equation and the classical Merton problem. | |||||
261-5120-00L | Computational Biomedicine II | W | 4 KP | 3P | G. Rätsch | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Voraussetzungen / Besonderes | 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 | Advanced Computer Networks | W | 5 KP | 2V + 2U | A. Singla, P. M. Stüdi | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
363-1000-00L | Financial Economics | W | 3 KP | 2V | A. Bommier | |
Kurzbeschreibung | This is a theoretical course on the economics of financial decision making, at the crossroads between Microeconomics and Finance. It discusses portfolio choice theory, risk sharing, market equilibrium and asset pricing. | |||||
Lernziel | The objective is to make students familiar with the economics of financial decision making and develop their intuition regarding the determination of asset prices, the notions of optimal risk sharing. However this is not a practical formation for traders. Moreover, the lecture doesn't cover topics such as market irrationality or systemic risk. | |||||
Inhalt | The following topics will be discussed: Introduction to finance and investment planning; Option valuation; Arbitrage; Choice under uncertainty; Portfolio Choice; Risk sharing and insurance; Market equilibrium under symmetric information. | |||||
Literatur | Suggesting readings: 1) "Investments", by Z. Bodie, A. Kane and A. Marcus, for the introductory part of the course (see chapters 20 and 21 in particular). 2) "Finance and the Economics of Uncertainty" by G. Demange and G. Laroque, Blackwell, 2006. 3) "The Economics of Risk and Time", by C. Gollier, and Other readings: - "Intermediate Financial Theory" by J.-P. Danthine and J.B. Donaldson. - Ingersoll, J., E., Theory of Financial Decision Making, Rowman and Littlefield Publishers. - Leroy S and J. Werner, Principles of Financial Economics, Cambridge University Press, 2001 | |||||
Voraussetzungen / Besonderes | Basic mathematical skills needed (calculus, linear algebra, convex analysis). Students must be able to solve simple optimization problems (e.g. Lagrangian methods). Some knowledge in microeconomics would help but is not compulsory. The bases will be covered in class. | |||||
363-1091-00L | Social Data Science | W | 3 KP | 2G | D. Garcia Becerra | |
Kurzbeschreibung | Social Data Science is introduced as a set of techniques to analyze human behavior and social interaction through digital traces. The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective. | |||||
Lernziel | A successful participant of this course will be able to - understand a wide variety of techniques to retrieve digital trace data from online data sources - store, process, and summarize online data for quantitative analysis - perform statistical analyses to test hypotheses, derive insights, and formulate predictions - implement streamlined software that integrates data retrieval, processing, statistical analysis, and visualization - interpret the results of data analysis with respect to theoretical and testable principles of human behavior - understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity | |||||
Inhalt | Social Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data. SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales. The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data. Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior. Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression. These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior. The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course ends with a datathon that sets the starting point of final student projects. The course will cover various examples of the application of SDS: - Search trends to measure information seeking - Popularity and social impact - Evaluation of sentiment analysis techniques - Quantitative analysis of emotions and social media sharing - Twitter social network analysis The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective. The block course contains lectures and exercise sessions during the morning and afternoon of five days. Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures. | |||||
Skript | The lecture slides will be available on the Moodle platform, for registered students only. | |||||
Literatur | See handouts. Specific literature is provided for download, for registered students only. | |||||
Voraussetzungen / Besonderes | Participants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective. Prior knowledge of advanced R, information retrieval, or information systems is not necessary. Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform the guided exercises. Course evaluation is based on the project developed in the last session datathon (50%) and on the final project report (50%). The course takes place between Feb 12th and Feb 16th (both inclusive), from 9:15 to 12:00 and from 13:15 to 16:00. | |||||
401-3629-00L | Quantitative Risk Management | W | 4 KP | 2V | P. Cheridito | |
Kurzbeschreibung | This course introduces methods from probability theory and statistics that can be used to model financial risks. Topics addressed include loss distributions, risk measures, extreme value theory, multivariate models, copulas and dependence structures as well as operational risk. | |||||
Lernziel | The goal is to learn the most important methods from probability theory and statistics used in financial risk modeling. | |||||
Inhalt | 1. Introduction 2. Basic Concepts in Risk Management 3. Empirical Properties of Financial Data 4. Financial Time Series 5. Extreme Value Theory 6. Multivariate Models 7. Copulas and Dependence 8. Operational Risk | |||||
Skript | Course material is available on Link | |||||
Literatur | Quantitative Risk Management: Concepts, Techniques and Tools AJ McNeil, R Frey and P Embrechts Princeton University Press, Princeton, 2015 (Revised Edition) Link | |||||
Voraussetzungen / Besonderes | The course corresponds to the Risk Management requirement for the SAA ("Aktuar SAV Ausbildung") as well as for the Master of Science UZH-ETH in Quantitative Finance. | |||||
401-3888-00L | Introduction to Mathematical Finance Ein verwandter Kurs ist 401-3913-01L Mathematical Foundations for Finance (3V+2U, 4 ECTS-KP). Obwohl beide Kurse unabhängig voneinander belegt werden können, darf nur einer ans gesamte Mathematik-Studium (Bachelor und Master) angerechnet werden. | W | 10 KP | 4V + 1U | M. Schweizer | |
Kurzbeschreibung | This is an introductory course on the mathematics for investment, hedging, portfolio management, asset pricing and financial derivatives in discrete-time financial markets. We discuss arbitrage, completeness, risk-neutral pricing and utility maximisation. We prove the fundamental theorem of asset pricing and the hedging duality theorems, and also study convex duality in utility maximization. | |||||
Lernziel | This is an introductory course on the mathematics for investment, hedging, portfolio management, asset pricing and financial derivatives in discrete-time financial markets. We discuss arbitrage, completeness, risk-neutral pricing and utility maximisation, and maybe other topics. We prove the fundamental theorem of asset pricing and the hedging duality theorems in discrete time, and also study convex duality in utility maximization. | |||||
Inhalt | This course focuses on discrete-time financial markets. It presumes a knowledge of measure-theoretic probability theory (as taught e.g. in the course "Probability Theory"). The course is offered every year in the Spring semester. This course is the first of a sequence of two courses on mathematical finance. The second course "Mathematical Finance" (MF II), 401-4889-00, focuses on continuous-time models. It is advisable that the present course, MF I, is taken prior to MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||
Skript | The course is based on different parts from different textbooks as well as on original research literature. Lecture notes will not be available. | |||||
Literatur | Literature: Michael U. Dothan, "Prices in Financial Markets", Oxford University Press Hans Föllmer and Alexander Schied, "Stochastic Finance: An Introduction in Discrete Time", de Gruyter Marek Capinski and Ekkehard Kopp, "Discrete Models of Financial Markets", Cambridge University Press Robert J. Elliott and P. Ekkehard Kopp, "Mathematics of Financial Markets", Springer | |||||
Voraussetzungen / Besonderes | NOTE: Due to personal (health) reasons, this course is offered in concentrated form during the second half of the semester. The course will start on *Monday, April 09, 2018*. Some extra information about possible preparation as well as extra references will be posted here later. A related course is "Mathematical Foundations for Finance" (MFF), 401-3913-01. Although both courses can be taken independently of each other, only one will be given credit points for the Bachelor and the Master degree. In other words, it is also not possible to earn credit points with one for the Bachelor and with the other for the Master degree. This course is the first of a sequence of two courses on mathematical finance. The second course "Mathematical Finance" (MF II), 401-4889-00, focuses on continuous-time models. It is advisable that the present course, MF I, is taken prior to MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||
401-3936-00L | Data Analytics for Non-Life Insurance Pricing | W | 4 KP | 2V | C. M. Buser, M. V. Wüthrich | |
Kurzbeschreibung | We study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests, gradient boosting machines and support vector machines. Moreover, we present unsupervised learning methods applied to telematics car driving data. | |||||
Lernziel | The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction. | |||||
Inhalt | We present the following chapters: - generalized linear models (GLMs) - generalized additive models (GAMs) - credibility theory - classification and regression trees (CARTs) - bagging, random forests and boosting - support vector machines (SVMs) - unsupervised learning methods - telematics car driving data | |||||
Skript | The lecture notes are available from: Link | |||||
Voraussetzungen / Besonderes | This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under Link Good knowledge in probability theory, stochastic processes and statistics is assumed. | |||||
401-4658-00L | Computational Methods for Quantitative Finance: PDE Methods | W | 6 KP | 3V + 1U | C. Schwab | |
Kurzbeschreibung | Introduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB programming and knowledge of numerical mathematics at ETH BSc level. | |||||
Lernziel | Introduce the main methods for efficient numerical valuation of derivative contracts in a Black Scholes as well as in incomplete markets due Levy processes or due to stochastic volatility models. Develop implementation of pricing methods in MATLAB. Finite-Difference/ Finite Element based methods for the solution of the pricing integrodifferential equation. | |||||
Inhalt | 1. Review of option pricing. Wiener and Levy price process models. Deterministic, local and stochastic volatility models. 2. Finite Difference Methods for option pricing. Relation to bi- and multinomial trees. European contracts. 3. Finite Difference methods for Asian, American and Barrier type contracts. 4. Finite element methods for European and American style contracts. 5. Pricing under local and stochastic volatility in Black-Scholes Markets. 6. Finite Element Methods for option pricing under Levy processes. Treatment of integrodifferential operators. 7. Stochastic volatility models for Levy processes. 8. Techniques for multidimensional problems. Baskets in a Black-Scholes setting and stochastic volatility models in Black Scholes and Levy markets. 9. Introduction to sparse grid option pricing techniques. | |||||
Skript | There will be english, typed lecture notes as well as MATLAB software for registered participants in the course. | |||||
Literatur | R. Cont and P. Tankov : Financial Modelling with Jump Processes, Chapman and Hall Publ. 2004. Y. Achdou and O. Pironneau : Computational Methods for Option Pricing, SIAM Frontiers in Applied Mathematics, SIAM Publishers, Philadelphia 2005. D. Lamberton and B. Lapeyre : Introduction to stochastic calculus Applied to Finance (second edition), Chapman & Hall/CRC Financial Mathematics Series, Taylor & Francis Publ. Boca Raton, London, New York 2008. J.-P. Fouque, G. Papanicolaou and K.-R. Sircar : Derivatives in financial markets with stochastic volatility, Cambridge Univeristy Press, Cambridge, 2000. N. Hilber, O. Reichmann, Ch. Schwab and Ch. Winter: Computational Methods for Quantitative Finance, Springer Finance, Springer, 2013. | |||||
Voraussetzungen / Besonderes | Start of the lecture: WED, 28 Feb. 2018 (second week of the semester). | |||||
401-8915-00L | Advanced Financial Economics (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC206 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 6 KP | 4G | Uni-Dozierende | |
Kurzbeschreibung | Portfolio Theory, CAPM, Financial Derivatives, Incomplete Markets, Corporate Finance, Behavioural Finance, Evolutionary Finance | |||||
Lernziel | Students should get familiar with the cornerstones of modern financial economics. | |||||
Voraussetzungen / Besonderes | This course replaces "Advanced Financial Economics" (MFOEC105), which will be discontinued. Students who have taken "Advanced Financial Economics" (MFOEC105) in the past, are not allowed to book this course "Advanced Financial Economics" (MFOEC206). There will be a podcast for this lecture. | |||||
636-0702-00L | Statistical Models in Computational Biology | W | 6 KP | 2V + 1U + 2A | N. Beerenwinkel | |
Kurzbeschreibung | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||
Lernziel | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||
Inhalt | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||
Skript | no | |||||
Literatur | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||
701-0412-00L | Klimasysteme | W | 3 KP | 2G | R. Knutti, I. Medhaug | |
Kurzbeschreibung | Die wichtigsten physikalischen Komponenten des Klimasystems und deren Wechselwirkungen werden eingeführt. Vor dem Hintergrund der Klimageschichte - und variabilität werden die Mechanismen des anthropogenen Klimawandels analysiert. Absolvierende des Kurses sind in der Lage, einfache Problemstellungen aus dem Bereich der Klimasysteme zu identifizieren und erläutern. | |||||
Lernziel | Studierende können: - die wichtigsten physikalischen Komponenten des goblaben Klimasystems beschreiben und ihre Wechselwirkungen skizzieren. - die Mechanismen des anthropogenen Klimawandels erklären. einfache Problemstellungen aus dem Bereich der Klimasysteme identifizieren und erläutern. | |||||
Skript | Kopien der Folien werden elektronisch zur Verfuegung gestellt. | |||||
Literatur | Eine vollständige Literaturliste wird abgegeben. Insbesondere empfohlen sind: - Hartmann, D., 2016: Global Physical Climatology. Academic Press, London, 485 pp. - Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp. | |||||
Voraussetzungen / Besonderes | Dozierende: Reto Knutti, mehrere Vorträge zu Spezialthemen von anderen Dozenten Unterrichtssprache: deutsch Sprache der Folien: englisch | |||||
701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 KP | 3G | C. Schär, U. Lohmann | |
Kurzbeschreibung | The guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes. | |||||
Lernziel | The guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes. | |||||
Inhalt | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||
Skript | Slides and lecture notes will be made available at Link | |||||
Literatur | List of literature will be provided. | |||||
Voraussetzungen / Besonderes | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming | |||||
701-1226-00L | Inter-Annual Phenomena and Their Prediction | W | 2 KP | 2G | C. Appenzeller | |
Kurzbeschreibung | This course provides an overview of the current ability to understand and predict intra-seasonal and inter-annual climate variability in the tropical and extra-tropical region and provides insights on how operational weather and climate services are organized. | |||||
Lernziel | Students will acquire an understanding of the key atmosphere and ocean processes involved, will gain experience in analyzing and predicting short-term climate variability and learn how operational weather and climate services are organised and how scientific developments can improve these services. | |||||
Inhalt | The course covers the following topics: Part 1: - a brief introduction into short-term climate variability and some basic concepts - a brief review of climate data and the statistical concepts used for analysing climate variability (e.g. correlation analysis, teleconnection maps, EOF analysis) Part 2: - inter-annual variability in the tropical region (e.g. ENSO, MJO) - inter-annual variability in the extra-tropical region (e.g. Blocking, NAO, PNA, regimes) Part 3: - prediction of short-term climate variability (statistical methods, ensemble prediction systems. weekly to seasonal forecasts) - verification methods for probabilistic forecast systems Part 4: - challenges for operational weather and climate services - weather and climate extremes - early warning systems - a visit to the forecasting centre of MeteoSwiss | |||||
Skript | A pdf version of the slides will be available at Link | |||||
Literatur | References are given during the lecture. | |||||
701-1252-00L | Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation | W | 3 KP | 2V + 1U | D. N. Bresch, R. Knutti | |
Kurzbeschreibung | The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation. | |||||
Lernziel | Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (MATLAB), hence basic understanding of scientific programming forms a prerequisite of the course. | |||||
Inhalt | The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society. The course provides an introduction to the following themes: 1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events 2) methods to optimize and constrain model parameters using observations 3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer) 4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources | |||||
Skript | Powerpoint slides will be made available | |||||
Literatur | - | |||||
Voraussetzungen / Besonderes | Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence basic understanding of scientific programming forms a prerequisite of the course. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required. Examination: graded tutorials during the semester (benotete Semesterleistung) | |||||
851-0252-06L | Introduction to Social Networks: Theory, Methods and Applications Number of participants limited to 40. This course is intended for students interested in data analysis and with basic knowledge of inferential statistics. | W | 3 KP | 2G | C. Stadtfeld, A. Vörös | |
Kurzbeschreibung | Humans are connected by various social relations. When aggregated, we speak of social networks. This course discusses how social networks are structured, how they change over time and how they affect the individuals that they connect. It integrates social theory with practical knowledge of cutting-edge statistical methods and applications from a number of scientific disciplines. | |||||
Lernziel | The aim is to enable students to contribute to social networks research and to be discriminating consumers of modern literature on social networks. Students will acquire a thorough understanding of social networks theory (1), practical skills in cutting-edge statistical methods (2) and their applications in a number of scientific fields (3). In particular, at the end of the course students will - Know the fundamental theories in social networks research (1) - Understand core concepts of social networks and their relevance in different contexts (1, 3) - Be able to describe and visualize networks data in the R environment (2) - Understand differences regarding analysis and collection of network data and other type of survey data (2) - Know state-of-the-art inferential statistical methods and how they are used in R (2) - Be familiar with the core empirical studies in social networks research (2, 3) - Know how network methods can be employed in a variety of scientific disciplines (3) | |||||
Data Science Projektkurs This course unit will be given in HS18 for the first time according to programme regulations 2016 Data Science MSc. | ||||||
Seminar | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
263-3830-00L | Software Defined Networking: The Data Centre Perspective | W | 2 KP | 2S | T. Roscoe, D. Wagenknecht-Dimitrova | |
Kurzbeschreibung | Software Defined Networks (SDN) is a change supported not only by research but also industry and redifens how traditional network management and configuration is been done. | |||||
Lernziel | Through review and discussion of literature on an exciting new trend in networking, the students get the opportunity to get familiar with one of the most promising new developments in data centre connectivity, while at the same time they can develop soft skills related to the evaluation and presentation of professional content. | |||||
Inhalt | Software Defined Networks (SDN) is a change supported not only by research but also industry and redifens how traditional network management and configuration is been done. Although much has been already investigated and there are already functional SDN-enabled switches there are many open questions ahead of the adoption of SDN inside and outside the data centre (traditional or cloud-based). With a series of seminars we will reflect on the challenges, adoption strategies and future trends of SDN to create an understanding how SDN is affecting the network operators' industry. | |||||
Literatur | The seminar is based on recent publications by academia and industry. Links to the publications are placed on the Seminar page and can be downloaded from any location with access to the ETH campus network. | |||||
Voraussetzungen / Besonderes | The seminar bases on active and interactive participation of the students. | |||||
263-3840-00L | Hardware Architectures for Machine Learning | W | 2 KP | 2S | G. Alonso, T. Hoefler, O. Mutlu, C. Zhang | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Voraussetzungen / Besonderes | The seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics. | |||||
401-3620-18L | Student Seminar in Statistics: Nonparametric Estimation under Shape-Constraints Maximale Teilnehmerzahl: 22 Hauptsächlich für Studierende der Bachelor- und Master-Studiengänge Mathematik, welche nach der einführenden Lerneinheit 401-2604-00L Wahrscheinlichkeit und Statistik (Probability and Statistics) mindestens ein Kernfach oder Wahlfach in Statistik besucht haben. | W | 4 KP | 2S | F. Balabdaoui, P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer | |
Kurzbeschreibung | Statistical inference based on a random sample can be performed under additional shape restrictions on the unknown entity to be estimated (regression curve, probability density, ROC curve...). Under shape restrictions, we mean a variety of constraints. Examples thereof include monotonicity, bounded variation, convexity, k-monotonicity or log-concavity. | |||||
Lernziel | The main goal of this Student Seminar is to get acquainted with the existing approaches in shape constrained estimation. The students will get to learn that specific estimation techniques can be used under shape restrictions to obtain better estimators, especially for small/moderate sample sizes. Students will also have the opportunity to learn that one of the main merits of shape constrained inference is to avoid choosing some arbitrary tuning parameter as it is the case with bandwidth selection in kernel estimation methods. Furthemore, students will get to read about some efficient algorithms that can be used to fastly compute the obtained estimators. One of the famous algoritms is the so-called PAVA (Pool Adjacent Violators Algorithm) used under monotonicity to compute a monotone estimator of a regression curve or a probability density. During the Seminar, the students will have to study some selected chapters from the books "Statistical Inference under Order Restrictions" by Barlow, Bartholomew, Bremner and Brunk, and "Nonparametric estimation under shape constraints" by Groeneboom and Jongbloed. Some "famous" articles on the subject will be also studied. | |||||
Voraussetzungen / Besonderes | We require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics and basic knowledge in computer programming. Topics will be assigned during the first meeting. | |||||
GESS Wissenschaft im Kontext | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
» Empfehlungen aus dem Bereich Wissenschaft im Kontext (Typ B) für das D-INFK | ||||||
» siehe Studiengang Wissenschaft im Kontext: Typ A: Förderung allgemeiner Reflexionsfähigkeiten | ||||||
» siehe Studiengang Wissenschaft im Kontext: Sprachkurse ETH/UZH | ||||||
851-0740-00L | Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 4.3.2018 at the latest | W | 3 KP | 2S | S. Bechtold, T. Roscoe, E. Vayena | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Master-Arbeit | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
261-0800-00L | Master's Thesis Zur Master-Arbeit wird nur zugelassen, wer: das Bachelor-Studium erfolgreich abgeschlossen hat; allfällige Auflagen für die Zulassung zum Studiengang erfüllt hat in der Kategorie "Kernfächer" mindestens 50 KP erworben hat, darunter die je minimal erforderlichen 16 KP in den Unterkategorien "Datenanalyse" sowie "Datenmanagement und Datenverarbeitung" und in der Kategorie "Data Science Projektkurs" die erforderlichen 14 KP erworben hat. | O | 30 KP | 64D | Professor/innen | |
Kurzbeschreibung | ||||||
Lernziel |