Search result: Catalogue data in Spring Semester 2016
|Certificate of Advanced Studies in Computer Science|
| Compulsory Major Courses|
Neither credits can be obtained from entrance exams nor credited to the Certificate programme.
|252-0211-00L||Information Security||W||8 credits||4V + 3U||D. Basin, S. Capkun|
|Abstract||This course provides an introduction to Information Security. The focus|
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
|Objective||Master fundamental concepts in Information Security and their|
application to system building. (See objectives listed below for more details).
|Content||1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to |
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
|252-0215-00L||Information Systems||W||8 credits||4V + 2U + 1A||M. Norrie|
|Abstract||The 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.|
|Objective||The 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.|
|Content||The 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.|
|Literature||Database Management Systems, Raghu Ramakrishnan and Johannes Gehrke, 3rd edition, pub McGraw Hill, 2003.|
|252-0216-00L||Software Architecture and Engineering||W||8 credits||4V + 3U||P. Müller, M. Vechev|
|Abstract||This course introduces both theoretical and applied aspects of software engineering and analysis. It covers:|
- Software Architecture
- Informal and formal Modeling
- Design Patterns
- Code Refactoring
- Program Testing
- Dynamic Program Analysis
- Static Program Analysis
|Objective||The course has two main objectives:|
- Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software.
- Understand how to apply these techniques in practice.
|Content||Some of the core technical topics covered will be:|
- modeling and mapping of models to code
- common code design patterns
- functional and structural testing
- dynamic and static analysis
|Literature||Will be announced in the lecture.|
|252-0220-00L||Learning and Intelligent Systems||W||8 credits||4V + 2U + 1A||A. Krause|
|Abstract||The course introduces the foudations of learning and making predictions based on data.|
|Objective||The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.|
|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)
|Literature||Textbook: Kevin Murphy: A Probabilistic Perspective, MIT Press|
|Prerequisites / Notice||Designed to provide basis for following courses:|
- Advanced Machine Learning
- Data Mining: Learning from Large Data Sets
- Probabilistic Artificial Intelligence
- Probabilistic Graphical Models
- Seminar "Advanced Topics in Machine Learning"
|401-0674-00L||Numerical Methods for Partial Differential Equations|
Not meant for BSc/MSc students of mathematics.
|W||8 credits||4V + 2U + 1A||R. Hiptmair|
|Abstract||Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in Python in one dimension and in C++ in 2D.|
|Objective||Main skills to be acquired in this course:|
* Ability to implement advanced numerical methods for the solution of partial differential equations efficiently
* Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations
* Ability to select and assess numerical methods in light of the predictions of theory
* Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm
* Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations.
* Skills in the efficient implementation of finite element methods on unstructured meshes.
This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages.
|Content||1 Case Study: A Two-point Boundary Value Problem|
1.2 A model problem
1.3 Variational approach
1.4 Simplified model
1.5.1 Galerkin discretization
1.5.2 Collocation [optional]
1.5.3 Finite differences
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)
3.5.4 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
6.2 Wave equations [optional]
6.2.1 Vibrating membrane
6.2.2 Wave propagation
6.2.3 Method of lines
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
7.3 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
8.4.1 Linear stability
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 notes||Lecture documents and classroom notes will be made available to the audience as PDF.|
|Literature||Chapters of the following books provide SUPPLEMENTARY reading|
(Detailed references in course material):
* D. Braess: Finite Elemente,
Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online)
* S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online)
* A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied
Mathematical Sciences. Springer, New York, 2004.
* Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007
* W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of
Springer Series in Computational Mathematics. Springer, Berlin, 1992.
* P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential
Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003.
* S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of
Texts in Applied Mathematics. Springer, Heidelberg, 2003.
* R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002.
However, study of supplementary literature is not important for for following the course.
|Prerequisites / Notice||Mastery of basic calculus and linear algebra is taken for granted.|
Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential.
Important: Coding skills 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.
|401-0686-10L||High Performance Computing for Science and Engineering (HPCSE) for Engineers II||W||4 credits||4G||P. Koumoutsakos, D. Rossinelli|
|Abstract||This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.|
|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.|
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