Search result: Catalogue data in Spring Semester 2016
|Certificate of Advanced Studies in Computer Science|
|Focus Courses and Electives|
|252-0312-00L||Ubiquitous Computing||W||3 credits||2V||S. Mayer|
|Abstract||Ubiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact.|
|Objective||The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact.|
|Lecture notes||Copies of slides will be made available|
|Literature||Will be provided in the lecture. To put you in the mood:|
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
|252-0355-00L||Object Databases||W||4 credits||2V + 1U||A. K. de Spindler|
|Abstract||The course examines the principles and techniques of providing data management in object-oriented programming environments. After introducing the basics of object storage and management, we will cover semantic object models and their implementation. Finally, we discuss advanced data management services such as version models for temporal and engineering databases and for software configuration.|
|Objective||The goal of this course is to extend the student's knowledge of database technologies towards object-oriented solutions. Starting with basic principles, students also learn about commercial products and research projects in the domain of object-oriented data management. Apart from getting to know the characteristics of these approaches and the differences between them, the course also discusses what application requirements justify the use of object-oriented databases. Therefore, it educates students to make informed decisions on when to use what database technology.|
|Content||The course examines the principles and techniques of providing data management in object-oriented programming environments. It is divided into three parts that cover the road from simple object persistence, to object-oriented database management systems and to advanced data management services. In the first part, object serialisation and object-relational mapping frameworks will be introduced. Using the example of the open-source project db4o, the utilisation, architecture and functionality of a simple object-oriented database is discussed. The second part of the course is dedicated to advanced topics such as industry standards and solutions for object data management as well as storage and index technologies. Additionally, advanced data management services such as version models for temporal and engineering databases as well as for software configuration are discussed. In the third and last part of the course, an object-oriented data model that features a clear separation of typing and classification is presented. Together with the model, its implementation in terms of an object-oriented database management system is discussed also. Finally, an extension of this data model is presented that allows context-aware data to be managed.|
|Prerequisites / Notice||Prerequisites: Knowledge about the topics of the lectures "Introduction to Databases" and "Information Systems" is required.|
|252-0374-00L||Web Engineering||W||6 credits||2V + 2U + 1A||M. Norrie|
|Abstract||The course teaches students about the basic principles of web engineering by examining the various technologies used in modern web sites in detail together with the step-by-step processes used to develop state-of-the art web sites.|
|Objective||The goals of the course are that students should be able to:|
- systematically develop state-of-the-art web sites using a range of technologies, platforms and frameworks in common use
- understand the role of different technologies and how they are combined in practice
- analyse requirements and select appropriate technologies, platforms and frameworks
The material covered in lectures will be supported by a series of practical exercises.
|252-0407-00L||Cryptography Foundations||W||7 credits||3V + 2U + 1A||U. Maurer|
|Abstract||Fundamentals and applications of cryptography. Cryptography as a mathematical discipline: reductions, constructive cryptography paradigm, security proofs. The discussed primitives include cryptographic functions, pseudo-randomness, symmetric encryption and authentication, public-key encryption, key agreement, and digital signature schemes. Selected cryptanalytic techniques.|
|Objective||The goals are:|
(1) understand the basic theoretical concepts and scientific thinking in cryptography;
(2) understand and apply some core cryptographic techniques and security proof methods;
(3) be prepared and motivated to access the scientific literature and attend specialized courses in cryptography.
|Content||See course description.|
|Prerequisites / Notice||Familiarity with the basic cryptographic concepts as treated for|
example in the course "Information Security" is required but can
in principle also be acquired in parallel to attending the course.
|252-0408-00L||Cryptographic Protocols |
Does not take place this semester.
|W||5 credits||2V + 2U||U. Maurer|
|Abstract||The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc.|
|Objective||Indroduction to a very active research area with many gems and paradoxical|
results. Spark interest in fundamental problems.
|Content||The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc.|
|Lecture notes||the lecture notes are in German, but they are not required as the entire|
course material is documented also in other course material (in english).
|Prerequisites / Notice||A basic understanding of fundamental cryptographic concepts |
(as taught for example in the course Information Security or
in the course Cryptography) is useful, but not required.
|252-0491-00L||Satisfiability of Boolean Formulas - Combinatorics and Algorithms |
Takes place for the last time in spring 2016.
|W||7 credits||3V + 2U + 1A||E. Welzl|
|Abstract||Basics (CNF, resolution), extremal properties (probabilistic method, derandomization, Local Lemma, partial satisfaction), 2-SAT algorithms (random walk, implication graph), NP-completeness (Cook-Levin), cube (facial structure, Kraft inequality, Hamming balls, covering codes), SAT algorithms (satisfiability coding lemma, Paturi-Pudlák-Zane, Hamming ball search, Schöning), constraint satisfaction.|
|Objective||Studying of advanced methods in algorithms design and analysis, and in discrete mathematics along a classical problem in theoretical computer science.|
|Content||Satisfiability (SAT) is the problem of deciding whether a boolean formula in propositional logic has an assignment that evaluates to true. SAT occurs as a problem and is a tool in applications (e.g. Artificial Intelligence and circuit design) and it is considered a fundamental problem in theory, since many problems can be naturally reduced to it and it is the 'mother' of NP-complete problems. Therefore, it is widely investigated and has brought forward a rich body of methods and tools, both in theory and practice (including software packages tackling the problem).|
This course concentrates on the theoretical aspects of the problem. We will treat basic combinatorial properties (employing the probabilistic method including a variant of the Lovasz Local Lemma), recall a proof of the Cook-Levin Theorem of the NP-completeness of SAT, discuss and analyze several deterministic and randomized algorithms and treat the threshold behavior of random formulas.
In order to set the methods encountered into a broader context, we will deviate to the more general set-up of constraint satisfaction and to the problem of proper k-coloring of graphs.
|Lecture notes||There exists no book that covers the many facets of the topic. Lecture notes covering the material of the course will be distributed.|
|Literature||Here is a list of books with material vaguely related to the course. They can be found in the textbook collection (Lehrbuchsammlung) of the Computer Science Library:|
George Boole, An Investigation of the Laws of Thought on which are Founded the Mathematical Theories of Logic and Probabilities, Dover Publications (1854, reprinted 1973).
Peter Clote, Evangelos Kranakis, Boolean Functions and Computation Models, Texts in Theoretical Computer Science, An EATCS Series, Springer Verlag, Berlin (2002).
Nadia Creignou, Sanjeev Khanna, Madhu Sudhan, Complexity Classifications of Boolean Constrained Satisfaction Problems, SIAM Monographs on Discrete Mathematics and Applications, SIAM (2001).
Harry R. Lewis, Christos H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall (1998).
Rajeev Motwani, Prabhakar Raghavan, Randomized Algorithms, Cambridge University Press, Cambridge, (1995).
Uwe Schöning, Logik für Informatiker, BI-Wissenschaftsverlag (1992).
Uwe Schöning, Algorithmik, Spektrum Akademischer Verlag, Heidelberg, Berlin (2001).
Michael Sipser, Introduction to the Theory of Computation, PWS Publishing Company, Boston (1997).
Klaus Truemper, Design of Logic-based Intelligent Systems, Wiley-Interscience, John Wiley & Sons, Inc., Hoboken (2004).
|Prerequisites / Notice||Language: The course will be given in English by default (it's German only if nobody expresses preference for English). All accompanying material (lecture notes, web-page, etc.) is supplied in English. |
Prerequisites: The course assumes basic knowledge in propositional logic, probability theory and discrete mathematics, as it is supplied in the first two years of the Bachelor Studies at ETH.
Outlook: There will be a follow-up seminar, SAT, on the topic in the subsequent semester (attendance of this course will be a prerequisite for participation in the seminar). There are ample possibilities for theses of various types (Master-, etc.).
|252-0526-00L||Statistical Learning Theory||W||4 credits||2V + 1U||J. M. Buhmann|
|Abstract||The course covers advanced methods of statistical learning :|
PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
|Objective||The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.|
|Content||# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification.|
# Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come.
# Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error?
# Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include:
* Maximum Entropy
* Information Bottleneck
* Deterministic Annealing
# Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures.
# Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike.
# Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
|Lecture notes||no script; transparencies of the lectures will be made available.|
|Literature||Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000.|
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.
L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
|Prerequisites / Notice||Requirements: |
basic knowledge of statistics, interest in statistical methods.
It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course.
|252-0538-00L||Shape Modeling and Geometry Processing |
Does not take place this semester.
|W||4 credits||2V + 1U||O. Sorkine Hornung|
|Abstract||This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry and interactive shape editing.|
|Objective||The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.|
|Content||Recent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry and interactive shape editing.|
|Lecture notes||Slides and course notes|
|Prerequisites / Notice||Prerequisites:|
Introduction to Computer Graphics, experience with C++ programming. Some background in geometry or computational geometry is helpful, but not necessary.
|252-0579-00L||3D Vision||W||4 credits||3G||M. Pollefeys, T. Sattler|
|Abstract||The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual inertial odometry (VIO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.|
|Objective||After attending this course, students will:|
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
|Content||The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.|
|252-0820-00L||Case Studies from Practice||W||4 credits||2V + 1U||M. Brandis|
|Abstract||The course is designed to provide students with an understanding of "real-life" challenges in business settings and teach them how to address these.|
|Objective||By using case studies that are based on actual IT projects, students will learn how to deal with complex, not straightforward problems. It will help them to apply their theoretical Computer Science background in practice and will teach them fundamental principles of IT management and challenges with IT in practice.|
|Content||The 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.
|252-1403-00L||Introduction to Quantum Information Processing||W||3 credits||2G||S. Wolf|
|Abstract||Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseudo-telepathy", quantum cryptography, as well as the main concepts of quantum information theory.|
|Objective||It is the goal of this course to get familiar with the most important notions that are of importance for the connection between Information and Physics. The formalism of Quantum Physics will be motivated and derived, and the use of these laws for information processing will be understood. In particular, the important algorithms of Grover as well as Shor will be studied and analyzed.|
|Content||According to Landauer, "information is physical". In quantum information, one is interested in the consequences and the possibilites offered by the laws of quantum physics for information processing. Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseude-telepathy", quantum cryptography, as well as the main concepts of quantum information theory.|
|252-1424-00L||Models of Computation||W||6 credits||2V + 2U + 1A||M. Cook|
|Abstract||This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.|
|Content||This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.|
|252-3005-00L||Introduction to Natural Language Processing||W||4 credits||2V + 1U||T. Hofmann, M. Ciaramita|
|Abstract||This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.|
|Objective||The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.|
|Content||This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.|
|Literature||Lectures will be presented from the Jurafsky and Martin text accompanied by related technical papers where necessary.|
|252-5706-00L||Mathematical Foundations of Computer Graphics and Vision||W||4 credits||2V + 1U||J.‑C. Bazin, M. R. Oswald, C. Öztireli|
|Abstract||This course presents the fundamental mathematical tools and concepts used in computer graphics and vision. Each theoretical topic is introduced in the context of practical vision or graphic problems, showcasing its importance in real-world applications.|
|Objective||The main goal is to equip the students with the key mathematical tools necessary to understand state-of-the-art algorithms in vision and graphics. In addition to the theoretical part, the students will learn how to use these mathematical tools to solve a wide range of practical problems in visual computing. After successfully completing this course, the students will be able to apply these mathematical concepts and tools to practical industrial and academic projects in visual computing.|
|Content||The theory behind various mathematical concepts and tools will be introduced, and their practical utility will be showcased in diverse applications in computer graphics and vision. The course will cover topics in sampling, reconstruction, approximation, optimization, robust fitting, differentiation, quadrature and spectral methods. Applications will include 3D surface reconstruction, camera pose estimation, image editing, data projection, character animation, structure-aware geometry processing, and rendering.|
|263-2300-00L||How To Write Fast Numerical Code |
Prerequisite: Master student, solid C programming skills.
|W||6 credits||3V + 2U||M. Püschel|
|Abstract||This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning.|
|Objective||Software performance (i.e., runtime) arises through the interaction of algorithm, its implementation, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this interaction, and hence software performance, focusing on numerical or mathematical functionality. The second goal is to teach a general systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in a few homeworks and semester-long group projects.|
|Content||The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. |
This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research.
Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning.
|263-2810-00L||Advanced Compiler Design||W||7 credits||3V + 2U + 1A||T. Gross|
|Abstract||This course covers advanced topics in compiler design: SSA intermediate representation and its use in optimization, just-in-time compilation, profile-based compilation, exception handling in modern programming languages.|
|Objective||Understand translation of object-oriented programs, opportunities and difficulties in optimizing programs using state-of-the-art techniques (profile-based compilation, just-in-time compilation, runtime system interaction)|
|Content||This course builds conceptually on Compiler Design (a basic class for advanced undergraduates), but this class is not a prerequisite. Students should however have a solid understanding of basic compiler technology. |
The focus is on handling the key features of modern object-oriented programs. We review implementations of single and multiple inheritance (incl. object layout, method dispatch) and optimization opportunities.
Specific topics: intermediate representations (IR) for optimizing compilers, static single assignment (SSA) representation, constant folding, partial redundancy optimizations, profiling, profile-guided code generation. Special topics as time permits: debugging optimized code, multi-threading, data races, object races, memory consistency models, programming language design. Review of single inheritance, multiple inheritance, object layout, method dispatch, type analysis, type propagation and related topics.
This course provides another opportunity to explore software design in a medium-scale software project.
|Literature||Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition). In addition, papers as provided in the class.|
|Prerequisites / Notice||A basic course on compiler design is helpful but not mandatory. Student should have programming skills/experience to implement an optimizer (or significant parts of an optimizer) for a simple object-oriented language. The programming project is implemented using Java.|
|263-2910-00L||Program Analysis and Synthesis||W||6 credits||3V + 2U||M. Vechev|
|Abstract||This course covers modern automated program analysis and synthesis techniques, including: |
(i) core theoretical foundations, and
(ii) applications of these foundations for solving useful practical challenges.
The techniques are widely applicable and are increasingly being used in a wide range of areas (e.g., systems, networks, security, etc).
|Objective||The course has 4 main objectives:|
* Understand the foundational principles behind modern automated program analysis and synthesis techniques.
* Understand how to apply these principles to build practical, working systems that can solve interesting real-world problems.
* Understand how these techniques interface with other research areas (e.g., machine learning, security)
* Gain familiarity with state-of-the-art in the area and with future research trends.
|Content||The last decade has seen an explosion in modern program analysis and synthesis techniques. These techniques are increasingly being used to reason about a vast range of computational paradigms, from finding security flaws in systems software (e.g., drivers, networks) to automating the construction of programs (e.g., for end user programming) and machine learning models (e.g., probabilistic programming).|
This course will provide a comprehensive introduction to modern, state-of-the-art program analysis and synthesis concepts, principles and research trends, including:
* Static Analysis:
- concepts: approximation, domains, precision, fixed points, numerical and heap analysis, asymptotic complexity, performance optimizations
- frameworks: APRON, PPL, ELINA, Facebook's Flow, Soot, LLVM, WALA
* Probabilistic programs and analysis
- concepts: Baysean networks, probabilistic languages (e.g., R2, Stan)
- frameworks: Alchemy, Markov Logic Networks, Picture
* Modern program synthesis (e.g. programming from examples for end users):
- concepts: L*, version spaces, Programming by Example, CEGIS
- frameworks: Sketch, AGS, SmartEdit, ReSynth, Flashfill
* Learning-based program synthesis:
- concepts: Markov networks, generative / discriminative models, probabilistic grammars
- frameworks: Nice2Predict
* Learning-based program analysis
- concepts: language models, neural networks
- frameworks: SLANG, JSNice (http://jsnice.org)
* Dynamic Analysis:
- concepts: soundness, efficiency, complexity, stateless model checking
- frameworks: FastTrack, EventRacer, Chess
* Predicate abstraction:
- concepts: Graf-Saidi, Boolean programs, lazy abstraction
- frameworks: Microsoft's SLAM, BLAST, Fender
* Symbolic execution:
- concepts: SMT, concolic execution
- frameworks: S2E, KLEE, Sage
* Security Analysis:
- concepts: information flow, hyperproperties
- example: malware detection
* Applications of Analysis & Synthesis:
To gain a deeper understanding of how to apply these techniques in practice, the course will involve a hands-on programming project where based on the principles introduced in class, the students will build an analysis / synthesis system.
|Lecture notes||The lectures notes will be distributed in class.|
|Literature||Distributed in class.|
|Prerequisites / Notice||This course is aimed at both graduate (M.Sc., PhD) students as well as advanced undergraduate students.|
|263-3501-00L||Advanced Computer Networks||W||5 credits||2V + 2U||P. M. Stüdi|
|Abstract||This course covers a set of advanced topics in computer networks. The focus is on principles, architectures, and protocols used in modern networked systems, such as the Internet itself, wireless and mobile networks, and large-scale peer-to-peer systems.|
|Objective||The goals of the course is to build on basic networking course material in providing an understanding of the tradeoffs and existing technology in building large, complex networked systems, and provide concrete experience of the challenges through a series of lab exercises.|
|Content||The focus of the course is on principles, architectures, and protocols used in modern networked systems. Topics include: wireless networks and mobility issues at the network and transport layer (Mobile IP and micromobility protocols, TCP in wireless environments). Mobile phone networks. Overlay networks, flat routing protocols (DHTs), and peer-to-peer architectures. The Border Gateway Protocol (BGP) in practice.|
|263-3700-00L||User Interface Engineering||W||4 credits||2V + 1U||O. Hilliges, F. Pece|
|Abstract||An in-depth introduction to the core concepts of post-desktop user interface engineering. Current topics in UI research, in particular non-desktop based interaction, mobile device interaction, augmented and mixed reality, and advanced sensor and output technologies.|
|Objective||Students will learn about fundamental aspects pertaining to the design and implementation of modern (non-desktop) user interfaces. Students will understand the basics of human cognition and capabilities as well as gain an overview of technologies for input and output of data. The core competency acquired through this course is a solid foundation in data-driven algorithms to process and interpret human input into computing systems. |
At the end of the course students should be able to understand and apply advanced hardware and software technologies to sense and interpret user input. Students will be able to develop systems that incorporate non-standard sensor and display technologies and will be able to apply data-driven algorithms in order to extract semantic meaning from raw sensor data.
|Content||User Interface Engineering covers theoretical and practical aspects relating to the design and implementation of modern non-standard user interfaces. A particular area of interest are machine-learning based algorithms for input recognition in advanced non-desktop user interfaces, including UIs for mobile devices but also Augmented Reality UIs, gesture and multi-modal user interfaces. |
The course covers three main areas:
I) Basic principles of human cognition and perception (and their application for UIs)
II) (Hardware) technologies for user input sensing
III) Data-driven methods for input recognition (gestures, speech, etc.)
Specific topics include:
* Model Human Processor (MHP) model - prediction of task completion times.
* Fitts' Law - measure of information load on human motor and cognitive system during user interaction.
* Touch sensor technologies (capacitive, resistive, force sensing etc).
* Data-driven algorithms for user input recognition:
- SVMs for classification and regression
- Randomized Decision Forests for gesture recognition and pose estimation
- Markov chains and HMMs for gesture and speech recognition
- Optical flow and other image processing and computer vision techniques
- Input filtering (Kalman)
* Applications of the above in HCI research
|Lecture notes||Slides and other materials will be available online. Lecture slides on a particular topic will typically not be made available prior the completion of that lecture.|
|Literature||A detailed reading list will be made available on the course website.|
|Prerequisites / Notice||Prerequisites: proficiency in a programming language such as C, programming methodology, problem analysis, program structure, etc. Normally met through an introductory course in programming in C, C++, Java.|
The following courses are strongly recommended as prerequisite:
* "Human Computer Interaction"
* "Machine Learning"
* "Visual Computing" or "Computer Vision"
The course will be assessed by a written Midterm and Final examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises.
|263-4600-00L||Formal Methods for Information Security||W||4 credits||2V + 1U||C. Sprenger, S. Radomirovic, R. Sasse|
|Abstract||The course focuses on formal methods for the modelling and analysis of security protocols for critical systems, ranging from authentication protocols for network security to electronic voting protocols and online banking.|
|Objective||The students will learn the key ideas and theoretical foundations of formal modelling and analysis of security protocols. The students will complement their theoretical knowledge by solving practical exercises, completing a small project, and using various state-of-the-art tools.|
|Content||The course treats formal methods for the modelling and analysis of security protocols. Cryptographic protocols (such as SSL/TLS, SSH, Kerberos, SAML single-sign on, and IPSec) form the basis for secure communication and business processes. Numerous attacks on published protocols show that the design of cryptographic protocols is extremely error-prone. A rigorous analysis of these protocols is therefore indispensable, and manual analysis is insufficient. The lectures cover the theoretical basis for the (tool-supported) formal modeling and analysis of such protocols. Specifically, we discuss their operational semantics, the formalization of security properties, and techniques and algorithms for their verification.|
In addition to the classical security properties for confidentiality and authentication, we will study strong secrecy, privacy, and fairness properties. We will discuss electronic voting protocols, RFID protocols (a staple of the Internet of Things), and contract signing protocols where these properties are central. The accompanying tutorials provide an opportunity to apply the theory and tools to concrete protocols.
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