Suchergebnis: Katalogdaten im Frühjahrssemester 2018
Informatik Bachelor  | ||||||
 Bachelor-Studium (Studienreglement 2008) | ||||||
| Kompensationsfächer Als Kompensationsfächer gelten die obligatorischen Fächer der Vertiefung. | ||||||
| Vertiefung | ||||||
| Obligatorische Fächer der Vertiefung | ||||||
| Vertiefung Computer and Software Engineering | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|---|
| 252-0210-00L | Compiler Design | O | 8 KP | 4V + 3U | T. Gross | |
| Kurzbeschreibung | Diese Vorlesung benutzt Compiler als Beispiel für moderne Software Entwicklung. Dazu werden die Kernthemen des Compilerbaus behandelt: Syntax Analyse, Symboltabellen, Code Erzeugung. Die Vorlesung und Uebungen geben den Studierenden eine gute Gelegenheit, Muster in diversen Kontexten anzuwenden. | |||||
| Lernziel | Learn principles of compiler design, gain practical experience designing and implementing a medium-scale software system. | |||||
| Inhalt | This course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools (parser generators); the implementation language is Java. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project. Specific topics: Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: expression evaluation, straight line code, conditionals, loops, procedure calls, simple register allocation techniques. Storage allocation on the stack, parameter passing, runtime storage management, heaps. Special topics as time permits: introduction to global dataflow and its application to register allocation, instruction scheduling, practical application of the techniques and principles presented in the lecture in the context of the OpenJDK HotSpot Java Virtual Machine. | |||||
| Literatur | Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition) Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997 | |||||
| Voraussetzungen / Besonderes | Prerequisites: Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level. | |||||
| 252-0216-00L | Software Architecture and Engineering | O | 8 KP | 4V + 3U | P. Müller, M. Vechev | |
| Kurzbeschreibung | This course introduces both theoretical and applied aspects of software engineering and analysis. It covers: - Software Architecture - Informal and formal Modeling - Design Patterns - Code Refactoring - Program Testing - Dynamic Program Analysis - Static Program Analysis | |||||
| Lernziel | The course has two main objectives: - Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software. - Understand how to apply these techniques in practice. | |||||
| Inhalt | Some of the core technical topics covered will be: - modeling and mapping of models to code - common code design patterns - functional and structural testing - dynamic and static analysis | |||||
| Literatur | Will be announced in the lecture. | |||||
| Vertiefung Computational Science | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 252-0220-00L | Introduction to Machine Learning Previously called Learning and Intelligent Systems Prof. Krause approves that students take distance exams, also if the exam will take place at a later time due to a different time zone of the alternative exam place. To get Prof. Krause's signature on the distance exam form please send it to Rita Klute, rita.klute@inf.ethz.ch. | O | 8 KP | 4V + 2U + 1A | A. Krause | |
| Kurzbeschreibung | The course introduces the foundations of learning and making predictions based on data. | |||||
| Lernziel | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||
| Inhalt | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression; k-NN - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian networks and exact inference (conditional independence; variable elimination; TANs) - Approximate inference (sum/max product; Gibbs sampling) - Latent variable models (Gaussian Misture Models, EM Algorithm) - Temporal models (Bayesian filtering, Hidden Markov Models) - Sequential decision making (MDPs, value and policy iteration) - Reinforcement learning (model-based RL, Q-learning) | |||||
| Literatur | Textbook: Kevin Murphy: A Probabilistic Perspective, MIT Press | |||||
| Voraussetzungen / Besonderes | Designed to provide basis for following courses: - Advanced Machine Learning - Data Mining: Learning from Large Data Sets - Probabilistic Artificial Intelligence - Probabilistic Graphical Models - Seminar "Advanced Topics in Machine Learning" | |||||
| 401-0674-00L | Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics. | O | 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. | |||||
| Vertiefung Theoretische Informatik | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 252-0211-00L | Information Security | O | 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 | |||||
| Wahlfächer der Vertiefung Zu den Wahlfächern zählen auch die obligatorischen Fächer der Vertiefung. Zudem können auch Lehrveranstaltungen aus dem Master in Informatik gewählt werden. Es liegt in der Verantwortung der Studierenden, sicherzustellen, dass sie die Voraussetzungen für diese Lehrveranstaltungen erfüllen. | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 252-0055-00L | Informationstheorie | W | 4 KP | 2V + 1U | L. Haug | |
| Kurzbeschreibung | Die Vorlesung vermittelt die Grundlagen von Shannons Informations- und Codierungstheorie. Die wichtigsten Themen sind: Entropie, Information, Datenkompression, Kanalcodierung, Codes. | |||||
| Lernziel | Ziel der Vorlesung ist es, sowohl mit den theoretischen Grundlagen der Informationstheorie vertraut zu machen, als auch den praktischen Einsatz der Theorie anhand ausgewählter Beispiele aus der Datenkompression und -codierung zu illustrieren. | |||||
| Inhalt | Einführung und Motivation, Grundlagen der Wahrscheinlichkeitstheorie, Entropie und Information, Kraft-Ungleichung, Schranken für die erwartete Länge von Quellcodes, Huffman-Codierung, asympotische Äquipartitionseigenschaft und typische Sequenzen, Shannons Quellcodierungstheorem, Kanalkapazität und Kanalcodierung, Shannons Kanalcodierungstheorem, Beispiele | |||||
| Literatur | T. Cover, J. Thomas: Elements of Information Theory, John Wiley, 1991. D. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003. C. Shannon, The Mathematical Theory of Communication, 1948. | |||||
| 252-0341-01L | Information Retrieval | W | 4 KP | 2V + 1U | G. Fourny | |
| Kurzbeschreibung | Introduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation. | |||||
| Lernziel | In depth understanding of how to model, index and query unstructured data (text), the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision. Knowledge on how to make queries faster and how to make queries work on very large datasets. Knowledge on how to evaluate the quality of an information retrieval engine. Knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank. | |||||
| Inhalt | Tentative plan (subject to change). The lecture structure will follow the pedagogical approach of the book (see below). The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture. 1. Introduction 2. The basics of how to index and query unstructured data 3. Pre-processing the data prior to indexing: building the term vocabulary, posting lists 4. Dealing with spelling errors: tolerant retrieval 5. Scaling up to large datasets 6. How to improve performance by compressing the index 7. Ranking the results: scores and the vector space model 8. Evaluating the quality of information retrieval: relevance 9. Query expansion 10. Structured retrieval: when the data is not quite unstructured (XML or HTML) 11. Alternate approach: Probabilistic information retrieval 12. Alternate approach: Language models 13. Crawling the Web 14. Link analysis (PageRank) | |||||
| Literatur | C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press. | |||||
| Voraussetzungen / Besonderes | Prior knowledge in linear algebra, data structures and algorithms, and probability theory (at the Bachelor's level) is required. | |||||
| 252-0820-00L | Case Studies from Practice | W | 4 KP | 2V + 1U | M. Brandis | |
| Kurzbeschreibung | The course is designed to provide students with an understanding of "real-life" computer science challenges in business settings and teach them how to address these. | |||||
| Lernziel | By using case studies that are based on actual IT projects, students will learn how to deal with complex, not straightforward problems. It will help them to apply their theoretical Computer Science background in practice and will teach them fundamental principles of IT management and challenges with IT in practice. A particular focus is to make the often imprecise and fuzzy problems in practice accessible to factual analysis and reasoning, and to challenge "common wisdom" and hearsay. | |||||
| Inhalt | The course consists of multiple lectures on methods to systematically analyze problems in a business setting and communicate about them as well as IT management and IT economics, presented by the lecturer, and a number of case studies provided by guest lecturers from either IT companies or IT departments of a diverse range of companies. Students will obtain insights into both established and startup companies, small and big, and different industries. Presenting companies have included avaloq, Accenture, AdNovum, Bank Julius Bär, Credit Suisse, Deloitte, HP, Hotelcard, IBM Research, McKinsey & Company, Open Web Technology, SAP Research, Selfnation, SIX Group, Teralytics, 28msec, Zühlke and dormakaba, and Marc Brandis Strategic Consulting. The participating companies in spring 2017 will be announced at course start. | |||||
| 151-0116-10L | High Performance Computing for Science and Engineering (HPCSE) for Engineers II | W | 4 KP | 4G | P. Koumoutsakos, P. Chatzidoukas | |
| Kurzbeschreibung | This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures. | |||||
| Lernziel | The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences | |||||
| Inhalt | High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation | |||||
| Skript | http://www.cse-lab.ethz.ch/index.php/teaching/42-teaching/classes/704-hpcse2 Class notes, handouts | |||||
| Literatur | - Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, Devinderjit Sivia | |||||
| 227-0124-00L | Embedded Systems | W | 6 KP | 4G | L. Thiele | |
| Kurzbeschreibung | An embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. The course covers theoretical and practical aspects of embedded system design and includes a series of lab sessions. | |||||
| Lernziel | Understanding specific requirements and problems arising in embedded system applications. Understanding architectures and components, their hardware-software interfaces, the memory architecture, communication between components, embedded operating systems, real-time scheduling theory, shared resources, low-power and low-energy design as well as hardware architecture synthesis. Using the formal models and methods in embedded system design in practical applications using the programming language C, the operating system FreeRTOS, a commercial embedded system platform and the associated design environment. | |||||
| Inhalt | An embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. For example, they are part of industrial machines, agricultural and process industry devices, automobiles, medical equipment, cameras, household appliances, airplanes, sensor networks, internet-of-things, as well as mobile devices. The focus of this lecture is on the design of embedded systems using formal models and methods as well as computer-based synthesis methods. Besides, the lecture is complemented by laboratory sessions where students learn to program in C, to base their design on the embedded operating systems FreeRTOS, to use a commercial embedded system platform including sensors, and to edit/debug via an integrated development environment. Specifically the following topics will be covered in the course: Embedded system architectures and components, hardware-software interfaces and memory architecture, software design methodology, communication, embedded operating systems, real-time scheduling, shared resources, low-power and low-energy design, hardware architecture synthesis. More information is available at https://www.tec.ee.ethz.ch/education/lectures/embedded-systems.html . | |||||
| Skript | The following information will be available: Lecture material, publications, exercise sheets and laboratory documentation at https://www.tec.ee.ethz.ch/education/lectures/embedded-systems.html . | |||||
| Literatur | P. Marwedel: Embedded System Design, Springer, ISBN 978-3-319-56045-8, 2018. G.C. Buttazzo: Hard Real-Time Computing Systems. Springer Verlag, ISBN 978-1-4614-0676-1, 2011. Edward A. Lee and Sanjit A. Seshia: Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press, ISBN 978-0-262-53381-2, 2017. M. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, ISBN 978-0-128-05387-4, 2016. | |||||
| Voraussetzungen / Besonderes | Prerequisites: Basic knowledge in computer architectures and programming. | |||||
| 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. | |||||
| Seminar Es kann auch ein Seminar aus dem Master in Informatik gewählt werden. Es liegt in der Verantwortung der Studierenden, sicherzustellen, dass sie die Voraussetzungen für diese Lehrveranstaltung erfüllen. | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 252-4220-00L | A Taste of Research: Algorithms and Combinatorics  Maximale Teilnehmerzahl: 16 Um das vorhandene Angebot optimal auszunutzen, behält sich das D-INFK vor, Belegungen von Studierenden zu löschen, die sich in mehreren Veranstaltungen dieser Kategorie eingeschrieben haben, bereits die erforderlichen Leistungen in dieser Kategorie erbracht haben oder aus anderen organisatorischen Gründen nicht auf die Belegung der Veranstaltung angewiesen sind. | W | 2 KP | 2S | B. Gärtner, A. Steger, M. Ghaffari | |
| Kurzbeschreibung | Students work together with lecturers on open problems in algorithms and combinatorics. | |||||
| Lernziel | The goal is to learn and practice important research techniques: literature search, understanding and presenting research papers, developing ideas in the group, testing of conjectures with the computer, writing down results. | |||||
| Inhalt | Work on original research papers and open problems in the areas of algorithms and combinatorics. | |||||
| Skript | Not available. | |||||
| Literatur | Will be announced in the seminar. | |||||
| Voraussetzungen / Besonderes | Passed exam in Algorithms, Probability, and Computing. | |||||
| 252-4230-00L | Advanced Algorithms and Data Structures Um das vorhandene Angebot optimal auszunutzen, behält sich das D-INFK vor, Belegungen von Studierenden zu löschen, die sich in mehreren Veranstaltungen dieser Kategorie eingeschrieben haben, bereits die erforderlichen Leistungen in dieser Kategorie erbracht haben oder aus anderen organisatorischen Gründen nicht auf die Belegung der Veranstaltung angewiesen sind. | W | 2 KP | 2S | P. Widmayer, S. Leucci, P. Uznanski | |
| Kurzbeschreibung | We will look into modern approaches of algorithms and data structures. A few breakthrough and highly influential papers from the general area of algorithms, from the past 20 years will be selected for students to study. | |||||
| Lernziel | Develop an understanding of modern techniques and paradigms in the design of algorithms and data structures. | |||||
| Inhalt | Topics include (but are not exhausted by): -graph algorithms, -text algorithms, -approximation algorithms, -algebra in algorithms, -streaming algorithms, -conditional lower bounds, -sparsification, -randomness in algorithms, -sampling. | |||||
| Voraussetzungen / Besonderes | Algorithms and Data Structures, or equivalent. | |||||
| 252-4800-00L | Information & Physics Number of participants limited to 120. Previously called Quantum Information and Cryptography Um das vorhandene Angebot optimal auszunutzen, behält sich das D-INFK vor, Belegungen von Studierenden zu löschen, die sich in mehreren Veranstaltungen dieser Kategorie eingeschrieben haben, bereits die erforderlichen Leistungen in dieser Kategorie erbracht haben oder aus anderen organisatorischen Gründen nicht auf die Belegung der Veranstaltung angewiesen sind. | W | 2 KP | 4S | S. Wolf | |
| Kurzbeschreibung | Es werden verschiedene Themen im Grenzgebiet der Bereiche Quantenphysik, Informationstheorie und Kryptographie behandelt. | |||||
| Lernziel | Themen im Grenzgebiet der Bereiche Quantenphysik, Informationstheorie und Kryptographie werden behandelt. | |||||
| GESS Wissenschaft im Kontext | ||||||
| Wissenschaft im Kontext | ||||||
| » Empfehlungen aus dem Bereich Wissenschaft im Kontext (Typ B) für das D-INFK | ||||||
| » siehe Studiengang Wissenschaft im Kontext: Typ A: Förderung allgemeiner Reflexionsfähigkeiten | ||||||
| Sprachkurse | ||||||
| » siehe Studiengang Wissenschaft im Kontext: Sprachkurse ETH/UZH | ||||||
| Bachelor-Arbeit | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
| 252-0500-00L | Bachelor-Arbeit | O | 10 KP | 21D | Professor/innen | |
| Kurzbeschreibung | Die Bachelor-Arbeit bildet den Abschluss des Studiengangs. Sie steht unter der Leitung eines Professors/einer Professorin des Departements Informatik und soll die Fähigkeit der Studierenden zu selbständiger, strukturierter und wissenschaftlicher Tätigkeit fördern. | |||||
| Lernziel | Die Bachelor-Arbeit soll dazu dienen, das Wissen in einem bestimmten Fachgebiet zu vertiefen und die Fähigkeit zu selbständiger, strukturierter und wissenschaftlicher Tätigkeit fördern. | |||||
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