Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Informatik Bachelor Information
Bachelor-Studium (Studienreglement 2016)
Basisprüfung
Basisprüfungsblock 1
Die Fächer des Blocks 1 werden im Herbstsemester angeboten.
Basisprüfungsblock 2
NummerTitelTypECTSUmfangDozierende
401-0212-16LAnalysis I Information O7 KP4V + 2UE. Kowalski
KurzbeschreibungFunktionen, Grenzwerte, Folgen, Reihen, Potenzreihen, Differential- und Integralrechnung einer Variablen,
LernzielFunktionen, Grenzwerte, Folgen, Reihen, Potenzreihen, Differential- und Integralrechnung einer Variablen,
InhaltFunktionen, Grenzwerte, Folgen, Reihen, Potenzreihen, Differential- und Integralrechnung einer Variablen,
LiteraturMichael Struwe: Analysis für Informatik
Christian Blatter: Ingenieur-analysis
Tom Apostol: Mathematical Analysis
Lernmaterialien und weitere Informationen sind auf der Webseite des Kurses (https://metaphor.ethz.ch/x/2018/fs/401-0212-16L/) erhältlich
252-0028-00LDesign of Digital Circuits Information O7 KP4V + 2UO. Mutlu
KurzbeschreibungDie Vorlesung bietet einen Einstieg in das Gebiet der Digitaltechnik. Zunächst wird auf die technische Realisierung von digitalen Schaltungen eingegangen. Eine Einführung in Hardware-Beschreibungssprachen und deren konkrete Anwendung im Entwurfsprozess schliesst sich an.
LernzielDie Vorlesung bietet einen Einstieg in das Gebiet der Digitaltechnik. Zunächst wird auf die technische Realisierung von digitalen Schaltungen eingegangen. Eine Einführung in Hardware-Beschreibungssprachen und deren konkrete Anwendung im Entwurfsprozess schliesst sich an.
InhaltDie Vorlesung bietet einen Einstieg in das Gebiet der Digitaltechnik. Zunächst wird auf die technische Realisierung von digitalen Schaltungen eingegangen. Die sich daraus ergebenden physikalischen Randbedingungen bestimmen massgeblich die vorgestellten Entwurfsmethoden von kombinatorischer und sequentieller Logik. Eine Einführung in Hardware-Beschreibungssprachen und deren konkrete Anwendung im Entwurfsprozess schliesst sich an.
252-0029-00LParallele Programmierung Information O7 KP4V + 2UT. Hoefler, M. Vechev
KurzbeschreibungEinfuehrung in das parallele Programmieren: nicht-deterministische und deterministische Programme, Modelle fuer parallele Programme, Synchronization, Kommunikation und Fairness.
LernzielEinfuehrung in das parallele Programmieren: nicht-deterministische und deterministische Programme, Modelle fuer parallele Programme, Synchronization, Kommunikation und Fairness. Uebungen beschaeftigen sich mit Threads in moderne Programmiersprachen
(Java, C#) und die Ausfuehrung von parallelen Programmen auf
Multi-Prozessor/Multi-Core basierten Systemen.
252-0030-00LAlgorithmen und Wahrscheinlichkeit Information O7 KP4V + 2UA. Steger, E. Welzl
KurzbeschreibungFortsetzung der Vorlesung Algorithmen und Datenstrukturen des ersten Semesters. Es werden klassische Algorithmen aus verschiedenen Anwendungsbereichen vorgestellt. In die diskrete Wahrscheinlichkeitstheorie wird eingeführt und das Konzept randomisierter Algorithmen an verschiedenen Beispielen vorgestellt.
LernzielVerständnis des Entwurfs und der Analyse von Algorithmen. Grundlagen der diskreten Wahrscheinlichkeitstheorie und ihrer Anwendung in der Algorithmik.
Grundlagenfächer
NummerTitelTypECTSUmfangDozierende
252-0058-00LFormal Methods and Functional Programming Information O7 KP4V + 2UD. Basin, P. Müller
KurzbeschreibungIn this course, participants will learn about new ways of specifying, reasoning about, and developing programs and computer systems. The first half will focus on using functional programs to express and reason about computation. The second half presents methods for developing and verifying programs represented as discrete transition systems.
LernzielIn this course, participants will learn about new ways of specifying,
reasoning about, and developing programs and computer systems. Our objective is to help students raise their level of abstraction in modeling and implementing systems.
InhaltThe first part of the course will focus on designing and reasoning
about functional programs. Functional programs are mathematical
expressions that are evaluated and reasoned about much like ordinary
mathematical functions. As a result, these expressions are simple to
analyze and compose to implement large-scale programs. We will cover the mathematical foundations of functional programming, the lambda calculus, as well as higher-order programming, typing, and proofs of correctness.

The second part of the course will focus on deductive and algorithmic validation of programs modeled as transition systems. As an example of deductive verification, students will learn how to formalize the semantics of imperative programming languages and how to use a formal semantics to prove properties of languages and programs. As an example of algorithmic validation, the course will introduce model checking and apply it to programs and program designs.
252-0063-00LData Modelling and Databases Information O7 KP4V + 2UG. Alonso, C. Zhang
KurzbeschreibungData modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines
LernzielIntroduction to relational databases and data management. Basics of SQL programming and transaction management.
InhaltThe course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL.
LiteraturKemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009.

Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008.
252-0064-00LComputer Networks Information O7 KP4V + 2UA. Perrig, A. Singla
KurzbeschreibungThis introductory course on computer networking takes a top-down view from networked applications all through the physical layer.
LernzielStudents will get a comprehensive overview of the key protocols and the architecture of the Internet, as one example of more general principles in network design. Students will also acquire hands-on experience in programming different aspects of a computer networks. Apart from the state-of-the-art in networking practice, students will explore the rationale for the design choices that networks in the past have made, and where applicable, why these choices may no longer be ideal.
SkriptThe slides for each lecture will be made available through the course Web page, along with additional reference material.
LiteraturComputer Networking: A Top-Down Approach, James F. Kurose and Keith W. Ross. Pearson; 7th edition (May 6, 2016)
Voraussetzungen / BesonderesThe course includes 2-4 graded programming assignments, which together will enable students to obtain a bonus of up to 15% of the final grade.
401-0614-00LWahrscheinlichkeit und Statistik Information O5 KP2V + 2UP. Cheridito
KurzbeschreibungEinführung in die Wahrscheinlichkeitstheorie und Statistik
Lernziela) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden

b) Probabilistisches Denken und stochastische Modellierung

c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren
InhaltWahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle, lineare Regression
SkriptLernmaterialien sind erhältlich auf https://metaphor.ethz.ch/x/2018/fs/401-0614-00L/
Kernfächer
Vertiefung Systems and Software Engineering
NummerTitelTypECTSUmfangDozierende
252-0210-00LCompiler DesignO8 KP4V + 3UT. Gross
KurzbeschreibungDiese 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.
LernzielLearn principles of compiler design, gain practical experience designing and implementing a medium-scale software system.
InhaltThis 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.
LiteraturAho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition)

Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997
Voraussetzungen / BesonderesPrerequisites:
Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level.
252-0216-00LSoftware Architecture and Engineering Information O8 KP4V + 3UP. Müller, M. Vechev
KurzbeschreibungThis 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
LernzielThe 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.
InhaltSome 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
LiteraturWill be announced in the lecture.
Vertiefung Information and Data Processing
NummerTitelTypECTSUmfangDozierende
252-0220-00LIntroduction to Machine Learning Information
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.
O8 KP4V + 2U + 1AA. Krause
KurzbeschreibungThe course introduces the foundations of learning and making predictions based on data.
LernzielThe 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)
LiteraturTextbook: Kevin Murphy: A Probabilistic Perspective, MIT Press
Voraussetzungen / BesonderesDesigned 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"
Vertiefung Theoretical Computer Science
NummerTitelTypECTSUmfangDozierende
252-0211-00LInformation Security Information O8 KP4V + 3UD. Basin, S. Capkun
KurzbeschreibungThis 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.
LernzielMaster fundamental concepts in Information Security and their
application to system building. (See objectives listed below for more details).
Inhalt1. 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
NummerTitelTypECTSUmfangDozierende
252-0055-00LInformationstheorie Information W4 KP2V + 1UL. Haug
KurzbeschreibungDie Vorlesung vermittelt die Grundlagen von Shannons Informations- und Codierungstheorie. Die wichtigsten Themen sind: Entropie, Information, Datenkompression, Kanalcodierung, Codes.
LernzielZiel 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.
InhaltEinführung 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
LiteraturT. 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.
252-0341-01LInformation Retrieval Information W4 KP2V + 1UG. Fourny
KurzbeschreibungIntroduction 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.
LernzielIn 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.
InhaltTentative 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)
LiteraturC. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press.
Voraussetzungen / BesonderesPrior knowledge in linear algebra, data structures and algorithms, and probability theory (at the Bachelor's level) is required.
252-0820-00LCase Studies from Practice Information W4 KP2V + 1UM. Brandis
KurzbeschreibungThe 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.
LernzielBy 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.
InhaltThe 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.
151-0116-10LHigh Performance Computing for Science and Engineering (HPCSE) for Engineers II Information W4 KP4GP. Koumoutsakos, P. Chatzidoukas
KurzbeschreibungThis 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.
LernzielThe 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
InhaltHigh 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
Skripthttp://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
227-0124-00LEmbedded Systems Information W6 KP4GL. Thiele
KurzbeschreibungAn 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.
LernzielUnderstanding 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.
InhaltAn 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 .
SkriptThe 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 .
LiteraturP. 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 / BesonderesPrerequisites: Basic knowledge in computer architectures and programming.
Ergänzung
In gewissen Fächern werden Vorbedingungen verlangt. Es liegt in der Verantwortung der Studierenden, sicherzustellen, dass diese Voraussetzungen erfüllt sind.
NummerTitelTypECTSUmfangDozierende
151-0854-00LAutonomous Mobile Robots Information W5 KP4GR. Siegwart, M. Chli, J. Nieto
KurzbeschreibungThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
LernzielThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
SkriptThis lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
LiteraturThis lecture is based on the Textbook:
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
227-0075-00LElektrotechnik I Information W3 KP2V + 2UJ. Biela
KurzbeschreibungGrundlagenvorlesung im Fachgebiet Elektrotechnik mit folgenden Themen: Konzepte von Spannung und Strom; Analyse von Gleich- und Wechselstromnetzwerken; Serie- und Parallelschaltungen von (komplexen) Widerstandsnetzwerken; Kirchhoff'sche Gesetze und andere Netzwerktheoreme; Transiente Vorgänge; Grundlagen elektrischer und magnetischer Felder;
LernzielDas Verständnis für grundlegende Konzepte der Elektrotechnik, im Speziellen der Schaltungstheorie soll gefördert werden. Der/die erfolgreiche Student/in kennt am Ende die Grundelemente elektrischer Schaltungen und beherrscht die Grundgesetze und -theoreme zur Bestimmung von Spannungen und Strömen in einer Schaltung mit solchen Elementen. Er/sie kann auch grundlegende Schaltungsberechnungen durchführen.
InhaltDiese Vorlesung vermittelt Grundlagenkenntnisse im Fachgebiet Elektrotechnik. Ausgehend von den grundlegenden Konzepten der Spannung und des Stroms wird die Analyse von Netzwerken bei Gleich- und Wechselstrom behandelt. Dies schliesst Serie- und Parallelschaltungen von Widerstandsnetzwerken und Netzwerken mit Kapazitäten und Induktivitäten, wie auch die Kirchhoff'schen Gesetze zur Behandlung solcher Schaltungen und anderer Netzwerktheoreme mit ein. Weiterhin werden transiente Vorgänge in einfachen Netzwerken untersucht und grundlegende Konzepte von leistungselektronischen Konvertersystemen betrachtet.
SkriptVorlesungskript/-folien Elektrotechnik I über SPOD und als PDF im Moodle verfügbar
LiteraturFür das weitergehende Studium werden in der Vorlesung verschiedene Bücher vorgestellt.
227-0123-00LMechatronikW6 KP4GT. M. Gempp
KurzbeschreibungEinführung in die Mechatronik. Sensoren und Aktoren. Elektronische und hydraulische Leistungsstellglieder. Prozessdatenverarbeitung und Grundlagen der Echtzeitprogrammierung. Multitasking und Multiprozessing. Modelle mechatronischer Systeme. Geometrische, kinematische und dynamische Elemente. Mechanik von Mehrkörpersystemen, systemtheoretische Grundlagen. Mechatronik-Beispiele aus der Industrie.
LernzielEinführung in die theoretischen Grundlagen und die Technik mechatronischer Einrichtungen. Theoretische und praktische Kenntnisse der grundlegenden Elemente eines mechatronischen Systems.
InhaltEinführung in die Mechatronik. Sensoren und Aktoren. Elektronische und hydraulische Leistungsstellglieder. Prozessdatenverarbeitung und Grundlagen der Echtzeitprogrammierung. Multitasking und Multiprozessing. Modelle mechatronischer Systeme. Geometrische, kinematische und dynamische Elemente. Mechanik von Mehrkörpersystemen, systemtheoretische Grundlagen. Mechatronik-Beispiele aus der Industrie.
SkriptLehrbuch empfohlen. Ergänzende Vorlesungsdokumentation, Firmendokumentation.
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