Search result: Catalogue data in Spring Semester 2016

Computer Science Bachelor Information
2. Semester
First Year Examinations (2. Sem.)
NumberTitleTypeECTSHoursLecturers
401-0212-00LAnalysis II Information O6 credits3V + 2UM. Struwe
AbstractMultivariable differential and integral calculus, ordinary differential equations, implicit functions, constrained extrema, vector calculus.
ObjectiveMultivariable differential and integral calculus, ordinary differential equations, implicit functions, constrained extrema, vector calculus.
Lecture notesMichael Struwe. _Analysis für Informatik._ ETH Zürich, 2010.
LiteratureJürgen Pöschel. _Etwas mehr Analysis._ Springer Spektrum, 2014.

Christian Blatter. _Ingenieur-Analysis._ 2002.
252-0002-00LData Structures and Algorithms Information O7 credits4V + 2UP. Widmayer
AbstractThis course is about fundamental algorithm design paradigms (such as induction, divide-and-conquer, backtracking, dynamic programming), classic algorithmic problems (such as sorting and searching), and data structures (such as lists, hashing, search trees). The connection between algorithms and data structures is explained for geometric and graph problems.
ObjectiveAn understanding of the design and analysis of fundamental algorithms and data structures.
ContentEs werden grundlegende Algorithmen und Datenstrukturen vorgestellt und analysiert. Dazu gehören auf der einen Seite Entwurfsmuster für Algorithmen, wie Induktion, divide-and-conquer, backtracking und dynamische Optimierung, ebenso wie klassische algorithmische Probleme, wie Suchen und Sortieren. Auf der anderen Seite werden Datenstrukturen für verschiedene Zwecke behandelt, darunter verkettete Listen, Hashtabellen, balancierte Suchbäume, verschiedene heaps und union-find-Strukturen. Weiterhin wird Adaptivität bei Datenstrukturen (wie etwa Splay-Bäume) und bei Algorithmen (wie etwa online-Algorithmen) beleuchtet. Das Zusammenspiel von Algorithmen und Datenstrukturen wird anhand von Geometrie- und Graphenproblemen illustriert.
LiteratureTh. Ottmann, P.Widmayer: Algorithmen und Datenstrukturen, Spektrum-Verlag, 5. Auflage, Heidelberg, Berlin, Oxford, 2011
Prerequisites / NoticeVoraussetzung:
252-0021-00L Einführung in die Programmierung
252-0024-00LParallel Programming Information O7 credits4V + 2UO. Hilliges, F. O. Friedrich
AbstractIntroduction to parallel programming: deterministic and non-deterministic programs, models for parallel computation, synchronization, communication, and fairness.
ObjectiveThe student should learn how to write a correct parallel program, how to measure its efficiency, and how to reason about a parallel program. Student should become familiar with issues, problems, pitfalls, and solutions related to the construction of parallel programs. Labs provide an opportunity to gain experience with threads, libraries for thread management in modern programming lanugages (e.g., Java, C#) and with the execution of parallel programs on multi-processor/multi-core computers.
402-0038-00LPhysicsO6 credits3V + 2UC. Grab
AbstractIntroduction to physics with emphasis on mechanics, relativity, thermodynamics und electromagnetism. Demonstrations, examples and exercises illustrate the basic concepts.
ObjectiveBecome familiar with some of the important topics in classical and modern physics.
ContentMechanics:
Motion, Newton's axioms, work and energy, vibrations and waves, special relativity.

Thermodynamics: temperature, gases, thermal radiation, engines

Electromagnetism:
Electrostatics, stationary currents, time dependent fields, Maxwell's equations, electromagnetic waves.
Lecture notes'Physics for Informatics Students'
Lecture taught at ETH Zurich in FS15
Prof. Dr. A.Rubbia
(Script in German)
Literature"Principles of Physics"
Author: Halliday, David
Wiley & Sons, 2010
ISBN 978-0-470-56158-4
Prerequisites / NoticePrerequisites: mathematical foundations, vectors, calculus.
252-0014-00LDigital Circuits Information O6 credits3V + 2US. Capkun, F. K. Gürkaynak
AbstractThe class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.
ObjectiveThe class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.
ContentThe class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.
4. Semester
Compulsory Courses (4. Sem.)
NumberTitleTypeECTSHoursLecturers
252-0058-00LFormal Methods and Functional Programming Information O7 credits4V + 2UD. Basin, P. Müller
AbstractIn 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.
ObjectiveIn 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.
ContentThe 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.
252-0062-00LOperating Systems and Networks Information O8 credits4V + 3UT. Hoefler, A. Perrig
AbstractThis is an introductory course on computer networks and operating
systems, with a particular focus on networking in the Internet and
monolithic operating systems like Linux and Windows. Network and OS
programming at different levels is an integral part of the course.
ObjectiveThis course is intended as an introduction to both computer networking
and operating systems for computer scientists. Students will get a
comprehensive overview of the key protocols and the general
architecture of the Internet, as one example of more general principles in
network design, and acquire hands-on experience in programming
different aspects of a computer network. In addition, the course
provides a full introduction to modern operating system design, including
memory management, scheduling, I/O, protection, and so on. The
architecture of Unix-like operating systems (such as Linux) is used as
an example of more general principles in OS design.
Lecture notesThe slides for each lecture will be made available in the web pages of the course, along with additional reference material.
LiteratureThe networking material will be based on the following text book:

Computer Networks (5th Edition)
Andrew S. Tanenbaum, David J. Wetherall
Prentice Hall; 5 edition (October 7, 2010)

In addition, the following textbook provides useful background for the operating systems material in the course:

Modern Operating Systems (3rd Edition)
Andrew S. Tanenbaum
Prentice-Hall, 2007
252-0063-00LData Modelling and Databases Information O7 credits4V + 2UG. Alonso
AbstractData modelling (ER and UML class diagrams), relational data model, relational design theory (normal forms), SQL, database integrity, security, transactions and data warehousing (OLAP).
ObjectiveIntroduction to relational databases and data management. Basics of SQL programming and transaction management.
ContentThe 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.
LiteratureKemper, Eickler: Datenbanksysteme: Eine Einführung, Oldenbourg Verlag, 5. Auflage, 2004.
Compensatory Courses
Compulsory major courses count as compensatory courses.
Major
Compulsory Major Courses
Major in Computer and Software Engineering
NumberTitleTypeECTSHoursLecturers
252-0215-00LInformation Systems Information O8 credits4V + 2U + 1AM. Norrie
AbstractThe course goes beyond relational technologies to examine the range of different database technologies available today. It also introduces the basic concepts of information retrieval and discusses the design decisions behind modern information system architectures.
ObjectiveThe goal of the course is to introduce students to the wide range of technologies in use today for data storage and retrieval. This will include extensions of relational database technologies, non-relational database systems and information retrieval systems. Through the use of a case study, students will also learn about modern information system architectures and the design decisions behind them.
ContentThe course follows on from an earlier course on relational database technologies by introducing other database paradigms and extensions to relational systems. Students will gain experience of working with object, NoSQL and XML databases and the course will examine the features of these systems in terms of their approaches to storage, querying and transaction management and how they compare to relational systems and each other. The course will also look at how relational systems have been extended to support specific types of data, for example spatial, temporal and text data. In the second half of the course, the students will be introduced to modern information system architectures that build on one or more database technologies and a case study will be used to examine the design decisions behind such architectures. The case study will also be used to introduce students to the problems and techniques associated with integration, data quality and evolution in systems for large-scale, long-term data management. The last part of the course will introduce the basic concepts of information retrieval systems, web search and web data extraction.
LiteratureDatabase Management Systems, Raghu Ramakrishnan and Johannes Gehrke, 3rd edition, pub McGraw Hill, 2003.
252-0216-00LSoftware Architecture and Engineering Information O8 credits4V + 3UP. Müller, M. Vechev
AbstractThis 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
ObjectiveThe 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.
ContentSome 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
LiteratureWill be announced in the lecture.
Major in Computational Science
NumberTitleTypeECTSHoursLecturers
252-0220-00LLearning and Intelligent Systems Information O8 credits4V + 2U + 1AA. Krause
AbstractThe course introduces the foudations of learning and making predictions based on data.
ObjectiveThe 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.
Content- 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)
LiteratureTextbook: Kevin Murphy: A Probabilistic Perspective, MIT Press
Prerequisites / NoticeDesigned 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"
401-0674-00LNumerical Methods for Partial Differential Equations
Not meant for BSc/MSc students of mathematics.
O8 credits4V + 2U + 1AR. Hiptmair
AbstractDerivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in Python in one dimension and in C++ in 2D.
ObjectiveMain 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.
Content1 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]
Lecture notesLecture documents and classroom notes will be made available to the audience as PDF.
LiteratureChapters 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.
Prerequisites / NoticeMastery 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 in MATLAB and C++ are essential.

Homework asssignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks.
401-0686-10LHigh Performance Computing for Science and Engineering (HPCSE) for Engineers II Information W4 credits4GP. Koumoutsakos, D. Rossinelli
AbstractThis 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.
Objective
Major in Theoretical Computer Science
NumberTitleTypeECTSHoursLecturers
252-0211-00LInformation Security Information O8 credits4V + 3UD. Basin, S. Capkun
AbstractThis 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.
ObjectiveMaster fundamental concepts in Information Security and their
application to system building. (See objectives listed below for more details).
Content1. 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
Electives
Compulsory major courses may also qualify as electives. Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.
NumberTitleTypeECTSHoursLecturers
252-0055-00LInformation Theory Information W4 credits2V + 1US. H. Hassani, J. M. Buhmann
AbstractThe course covers the fundamental concepts of Shannon's information theory.
The most important topics are: Entropy, information, data compression, channel coding, codes.
ObjectiveThe goal of the course is to familiarize with the theoretical fundamentals of information theory and to illustrate the practical use of the theory with the help of selected examples of data coding and compression.
ContentIntroduction and motivation, basics of probability theory, stochastic processes, entropy and information, asymptotic equipartition property, channel capacity and coding, linear codes with examples, polar codes.
LiteratureC. Shannon, The Mathematical Theory of Communication, 1948.

T. Cover, J. Thomas: Elements of Information Theory, John Wiley, 1991.
252-0820-00LCase Studies from Practice Information W4 credits2V + 1UM. Brandis
AbstractThe course is designed to provide students with an understanding of "real-life" challenges in business settings and teach them how to address these.
ObjectiveBy 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.
ContentThe course consists of multiple lectures about general IT management topics held by Marc Brandis and 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, IBM Research, McKinsey & Company, Open Web Technology, SAP Research, Selfnation, WhiteStein Technologies, 28msec, and Marc Brandis Strategic Consulting. The participating companies in spring 2016 will be announced at course start.
227-0124-00LEmbedded Systems Information W6 credits4GL. Thiele
AbstractComputer systems for controlling industrial devices are called embedded systems (ES). Specifically the following topics will be covered: Design methodology, software design, real-time scheduling and operating systems, architectures, distributed embedded systems, low-power and low-energy design, architecture synthesis.
ObjectiveIntroduction to industrial applications of computer systems; understanding specific requirements and problems arising in such applications. The focus of this lecture is on the implementation of embedded systems using formal methods and computer-based synthesis methods.
ContentComputer systems for controlling industrial devices are called embedded systems (ES). ES not only have to react to random events in their environment in a timely manner, they also have to calculate control values from continuous sequences of measurements. Embedded computer systems are connected to their environment though sensors and actors. The great interest in the systematic design of heterogeneous reactive systems is caused by the growing diversity and complexity of applications for ES, the requirement for low development and testing costs, and by progress in key technologies. Specifically the following topics will be covered: Design methodology, software design, real-time scheduling and operating systems, architectures, distributed embedded systems, low-power and low-energy design, architecture synthesis. See: http://www.tik.ee.ethz.ch/tik/education/lectures/ES/ .
Lecture notesMaterial/script, publications, exercise sheets, podcast. See: http://www.tik.ee.ethz.ch/tik/education/lectures/ES/ .
Literature[Mar07] P. Marwedel. Eingebettete Systeme. Springer Verlag, Paperback, December 2007. ISBN 978-3-540-34048-5

[Mar11] P. Marwedel. Embedded System Design: Embedded Systems Foundations of Cyber-Physical Systems. Springer Verlag, Paperback, 2011. ISBN 978-94-007-0256-1

[Tei07] J. Teich. Digitale Hardware/Software-Systeme: Synthese und Optimierung. Springer Verlag, 2007. ISBN 3540468226

[But11] G.C. Buttazzo. Hard real-time computing systems: predictable scheduling algorithms and applications. Springer Verlag, Berlin, 2011. ISBN-10: 1461406757, ISBN-13: 9781461406754

[Wolf12] W. Wolf. Computers as components: principles of embedded computing system design. Morgan Kaufmann, 2012. ISBN-10: 0123884365, ISBN-13: 978-0123884367
Prerequisites / NoticePrerequisites:
Basic course in computer engineering; knowledge about distributed systems and concepts for their description.
227-0945-10LCell and Molecular Biology for Engineers II
This course is part II of a two-semester course.
W3 credits2GC. Frei
AbstractThe 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.
ObjectiveAfter 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.
ContentLectures 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.
Lecture notesScripts of all lectures will be available.
Literature"Molecular Biology of the Cell" (5th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter.
252-3125-00LPrinciples of Interaction Design Information
Does not take place this semester.
W3 credits2GM. Norrie
AbstractThe course allows students to explore user-centred design processes and to get hands-on experience in engineering interactive systems with a focus on multi-device environments.
ObjectiveThe goal of the course is that students should have basic knowledge of interaction design methods and tools as well as practical experience of engineering interactive systems.
ContentThe course picks up on the principles of interaction design from the Human-Computer Interaction course and allows students to explore user-centred design processes in the context of a larger interactive systems engineering project. At the core of the course is a supervised project ideally carried out in teams, which is used both to teach the principles of interaction design and guide the students through the design-build-evaluate interactive system development cycle using rapid prototyping techniques at all stages. Through the project, students will acquire practical experience of working with state-of-the-art hardware and software technologies when designing and implementing a multi-device application that is able to adapt to a variety of use contexts including multi-touch phones, tablets and interactive tabletops. The course is accompanied by a set of introductory lectures to give students an overview of established user interface engineering methods and tools as well as providing the necessary background to the project.
Prerequisites / NoticeIt is recommended that students also complete the Human-Computer Interaction course. Relevant principles and methods will be reviewed in this course and applied to the project.
Seminar
NumberTitleTypeECTSHoursLecturers
252-4220-00LA Taste of Research: Algorithms and Combinatorics Information Restricted registration - show details
Number of participants limited to 16.
W2 credits2SB. Gärtner, A. Steger, E. Welzl
AbstractStudents work together with lecturers on open problems in algorithms and combinatorics.
ObjectiveThe 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.
ContentWork on original research papers and open problems in the areas of algorithms and combinatorics.
Lecture notesNot available.
LiteratureWill be announced in the seminar and on the seminar's web page.
Prerequisites / NoticeCourse Algorithms, Probability, and Computing passed.
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