Search result: Catalogue data in Spring Semester 2018
|Computer Science Master|
|Focus Courses General Studies|
|Elective Focus Courses General Studies|
|252-0312-00L||Ubiquitous Computing||W||3 credits||2V||F. Mattern, 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.|
|252-0408-00L||Cryptographic Protocols||W||5 credits||2V + 2U||M. Hirt, 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 Foundations) is useful, but not required.
|252-0526-00L||Statistical Learning Theory||W||6 credits||2V + 3P||J. M. Buhmann|
|Abstract||The course covers advanced methods of statistical learning :|
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||# 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.|
# 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.
# Statistical physics models: approaches for large systems approximate optimization, which originate in the statistical physics (free energy minimization applied to spin glasses and other models); sampling methods based on these models
|Lecture notes||A draft of a script will be provided; |
transparencies of the lectures will be made available.
|Literature||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: |
knowledge of the Machine Learning course
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.
|252-0570-00L||Game Programming Laboratory |
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||10 credits||9P||B. Sumner|
|Abstract||The goal of this course is the in-depth understanding of the technology and programming underlying computer games. Students gradually design and develop a computer game in small groups and get acquainted with the art of game programming.|
|Objective||The goal of this new course is to acquaint students with the|
technology and art of programming modern three-dimensional computer
|Content||This is a new course that addresses modern three-dimensional computer|
game technology. During the course, small groups of students will
design and develop a computer game. Focus will be put on technical
aspects of game development, such as rendering, cinematography,
interaction, physics, animation, and AI. In addition, we will
cultivate creative thinking for advanced gameplay and visual effects.
The "laboratory" format involves a practical, hands-on approach with
neither traditional lectures nor exercises. Instead, we will meet
once a week to discuss technical issues and to track progress. We
plan to utilize Microsoft's XNA Game Studio Express, which is a
collection libraries and tools that facilitate game development.
While development will take place on PCs, we will ultimately deploy
our games on the XBox 360 console.
At the end of the course we will present our results to the public.
|Lecture notes||Online XNA documentation.|
|Prerequisites / Notice||The number of participants is limited.|
- good programming skills (Java, C++, C#, etc.)
- CG experience: Students should have taken, at a minimum, Visual
Computing. Higher level courses are recommended, such as Introduction
to Computer Graphics, Surface Representations and Geometric Modeling,
and Physically-based Simulation in Computer Graphics.
|252-0579-00L||3D Vision||W||4 credits||3G||T. Sattler, M. R. Oswald|
|Abstract||The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), 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.|
|252-0807-00L||Information Systems Laboratory |
Number of participants limited to 12.
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||10 credits||9P||M. Norrie|
|Abstract||The purpose of this laboratory course is to practically explore modern techniques to build large-scale distributed information systems. Participants will work in groups of three or more students, and develop projects in several phases.|
|Objective||The students will gain experience of working with technologies used in the design and development of information systems.|
|Content||First week: Kick-off meeting and project assignment|
Second week: Meeting with the project supervisor to discuss the goals and scope of the project.
During the semester: Individual group work. Each team member should contribute to the project roughly about 10h/week, excluding any necessary reading or self-studying (e.g. the time spent to learn a new technology). In addition, it is expected that each team can meet with their supervisor on a regular basis.
End of semester: Final presentation.
|252-0817-00L||Distributed Systems Laboratory |
In the Master Programme max. 10 credits can be accounted by Labs
on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||10 credits||9P||G. Alonso, T. Hoefler, F. Mattern, T. Roscoe, A. Singla, R. Wattenhofer, C. Zhang|
|Abstract||This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones.|
|Objective||Students acquire practical knowledge about technologies from the area of distributed systems.|
|Content||This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. The objecte of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.|
For information of the course or projects available, please contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso.
|252-1403-00L||Invitation to Quantum Informatics||W||3 credits||2V||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.|
|Objective||The goal of this course is to become acquainted with a wide variety of models of computation, to understand how models help us to understand the modeled systems, and to be able to develop and analyze models appropriate for new systems.|
|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||Natural Language Understanding||W||4 credits||2V + 1U||T. Hofmann, M. Ciaramita|
|Abstract||This course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning 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 make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers.|
|252-5706-00L||Mathematical Foundations of Computer Graphics and Vision||W||4 credits||2V + 1U||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.|
|263-2812-00L||Program Verification |
Number of participants limited to 30.
|W||5 credits||2V + 1U + 1A||A. J. Summers|
|Abstract||A hands-on introduction to the theory and construction of deductive software verifiers, covering both cutting-edge methodologies for formal program reasoning, and a perspective over the broad tool stacks making up modern verification tools.|
|Objective||Students will earn the necessary skills for designing and developing deductive verification tools which can be applied to modularly analyse complex software, including features challenging for reasoning such as heap-based mutable data and concurrency. Students will learn both a variety of fundamental reasoning principles, and how these reasoning ideas can be made practical via automatic tools. |
Students will be gain practical experience with reasoning tools at various levels of abstraction, from SAT and SMT solvers at the lowest level, up through intermediate verification languages and tools, to verifiers which target front-end code in executable languages.
By the end of the course, students should have a good working understanding and experience of the issues and decisions involved with designing and building practical verification tools, and the theoretical techniques which underpin them.
|Content||The course will be organized around building up a "tool stack", starting at the lowest-level with background on SAT and SMT solving techniques, and working upwards through tools at progressively-higher levels of abstraction. The notion of intermediate verification languages will be explored, and the Boogie (Microsoft Research) and Viper (ETH) languages will be used in depth to tackle increasingly ambitious verification tasks. |
The course will intermix technical content with hands-on experience; at each level of abstraction, we will build small tools on top which can tackle specific program correctness problems, starting from simple puzzle solvers (Soduko) at the SAT level, and working upwards to full functional correctness of application-level code. This practical work will include three mini-projects (each worth 10% of the final grade) spread throughout the course, which count towards the final grade. An oral examination (worth 70% of the final grade) will cover the technical content covered.
|Lecture notes||Slides and other materials will be available online.|
|Literature||Background reading material and links to tools will be published on the course website.|
|Prerequisites / Notice||Some programming experience is essential, as the course contains several practical assignments. A basic familiarity with propositional and first-order logic will be assumed. |
Courses with an emphasis on formal reasoning about programs (such as Formal Methods and Functional Programming) are advantageous background, but are not a requirement.
|263-3501-00L||Advanced Computer Networks||W||5 credits||2V + 2U||A. Singla, 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 and data center networks.|
|Objective||The goals of the course are to build on basic undergraduate-level networking, and provide an understanding of the tradeoffs and existing technology in the design of large, complex networked systems, together with 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 data center network topologies, software defined networking, network function virtualization, flow control and congestion control in data centers, end-point optimizations, and server virtualization.|
|263-3710-00L||Machine Perception |
Students, who have already taken 263-3700-00 User Interface Engineering are not allowed to register for this course!
|W||5 credits||2V + 1U + 1A||O. Hilliges|
|Abstract||Recent developments in neural network (aka “deep learning”) have drastically advanced the performance of machine perception systems in a variety of areas including drones, self-driving cars and intelligent UIs. This course is a deep dive into details of the deep learning algorithms and architectures for a variety of perceptual tasks.|
|Objective||Students will learn about fundamental aspects of modern deep learning approaches for perception. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in learning-based computer vision, robotics and HCI. The final project assignment will involve training a complex neural network architecture and applying it on a real-world dataset of human motion.|
The core competency acquired through this course is a solid foundation in deep-learning algorithms to process and interpret human input into computing systems. In particular, students should be able to develop systems that deal with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others.
|Content||We will focus on teaching how to set up the problem of machine perception, the learning algorithms (e.g. backpropagation), practical engineering aspects as well as advanced deep learning algorithms including generative models.|
The course covers the following main areas:
I) Machine-learning algorithms for input recognition, computer vision and image classification (human pose, object detection, gestures, etc.)
II) Deep-learning models for the analysis of time-series data (temporal sequences of motion)
III) Learning of generative models for synthesis and prediction of human activity.
Specific topics include:
• Deep learning basics:
○ Neural Networks and training (i.e., backpropagation)
○ Feedforward Networks
○ Recurrent Neural Networks
• Deep Learning techniques user input recognition:
○ Convolutional Neural Networks for classification
○ Fully Convolutional architectures for dense per-pixel tasks (i.e., segmentation)
○ LSTMs & related for time series analysis
○ Generative Models (GANs, Variational Autoencoders)
• Case studies from research in computer vision, HCI, robotics and signal processing
Book by Ian Goodfellow and Yoshua Bengio
|Prerequisites / Notice||This is an advanced grad-level course that requires a background in machine learning. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. The course will focus on state-of-the-art research in deep-learning and is not meant as extensive tutorial of how to train deep networks with Tensorflow..|
Please take note of the following conditions:
1) The number of participants is limited to 100 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge
3) All practical exercises will require basic knowledge of Python and will use libraries such as TensorFlow, scikit-learn and scikit-image. We will provide introductions to TensorFlow and other libraries that are needed but will not provide introductions to basic programming or Python.
The following courses are strongly recommended as prerequisite:
* "Machine Learning"
* "Visual Computing" or "Computer Vision"
The course will be assessed by a final written 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.
|263-4110-00L||Interdisciplinary Algorithms Lab |
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||5 credits||2P||A. Steger, D. Steurer, J. Lengler|
|Abstract||In this course students will develop solutions for algorithmic problems posed by researchers from other fields.|
|Objective||Students will learn that in order to tackle algorithmic problems from an interdisciplinary or applied context one needs to combine a solid understanding of algorithmic methodology with insights into the problem at hand to judge which side constraints are essential and which can be loosened.|
|Prerequisites / Notice||Students will work in teams. Ideally, skills of team members complement each other. |
Interested Bachelor students can apply for participation by sending an email to firstname.lastname@example.org explaining motivation and transcripts.
|263-4310-00L||Linear Algebra Methods in Combinatorics||W||5 credits||2V + 2U||P. Penna|
|Abstract||This course describes the linear algebra bound technique also called dimension argument. To learn the technique we discuss several examples in combinatorics, geometry, and computer science. Besides this technique, the course aims at showing the mathematical elegance of certain proofs and the simplicity of the statements.|
|Objective||Becoming familiar with the method and being able to apply it to problems similar to those encountered during the course.|
|Content||This course is (essentially) about one single technique called the "linear algebra bound" (also known as "dimension argument"). We shall see several examples in combinatorics, geometry, and computer science and learn the technique throughout these examples. Towards the end of the course, we shall see the power of this method in proving rather amazing results (e.g., a circuit complexity lower bound, explicit constructions of Ramsey graphs, and a famous conjecture in geometry disproved). The course also aims at illustrating the main ideas behind the proofs and how the various problems are in fact connected to each other.|
|Lecture notes||Lecture notes of each single lecture will be made available (shortly after the lecture itself).|
|Literature||Most of the material of the course is covered by the following book:|
1. Linear algebra methods in combinatorics, by L. Babai and P. Frankl, Department of Computer Science, University of Chicago, preliminary version, 1992.
Some parts are also taken from
2. Extremal Combinatorics (with Applications in Computer Science), by Stasys Jukna, Springer-Verlag 2001.
|263-4312-00L||Advanced Data Structures||W||5 credits||2V + 2U||P. Uznanski|
|Abstract||Data structures play a central role in modern computer science and are essential building blocks in obtaining efficient algorithms. The course covers major results and research directions in data structures, that (mostly) have not yet made it into standard computer science curriculum.|
|Objective||Learning modern models of computation. Applying new algorithmic techniques to the construction of efficient data structures. Understanding techniques used in both lower- and upper- bound proofs on said data structures.|
|Content||This course will survey important developments in data structures that have not (yet) worked their way into the standard computer science curriculum. |
Though we will cover state of the art techniques, the presentation is relatively self-contained, and only assumes a basic undergraduate data structures course (e.g., knowledge of binary search trees).
The course material includes (but is not exhausted by):
- computation models and memory models
- string indexing (suffix trees, suffix arrays)
- search trees
- static tree processing (Lowest Common Ancestor queries, Level Ancestry queries)
- range queries on arrays (queries for minimal element in a given range)
- integers-only data structures: how to sort integers in linear time, faster predecessor structures (van Emde Boas trees)
- dynamic graphs connectivity
|Prerequisites / Notice||This is a highly theoretical course. You should be comfortable with:|
- algorithms and data structures
Completing Algorithms, Probability, and Computing course (252-0209-00L) is a good indicator.
|263-4600-00L||Formal Methods for Information Security||W||4 credits||2V + 1U||R. Sasse, C. Sprenger|
|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 state-of-the-art tools.|
|Content||The course treats formal methods mainly 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 and privacy properties. We will discuss electronic voting protocols, and RFID protocols (a staple of the Internet of Things), where these properties are central. The accompanying tutorials provide an opportunity to apply the theory and tools to concrete protocols. Moreover, we will discuss methods to abstract and refine security protocols and the link between symbolic protocol models and cryptographic models.
Furthermore, we will also present a security notion for general systems based on non-interference as well as language-based information flow security where non-interference is enforced via a type system.
|272-0300-00L||Algorithmics for Hard Problems |
Does not take place this semester.
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A.
|W||4 credits||2V + 1U||J. Hromkovic|
|Abstract||This course unit looks into algorithmic approaches to the solving of hard problems. The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools.|
|Objective||To systematically acquire an overview of the methods for solving hard problems.|
|Content||First, the concept of hardness of computation is introduced (repeated for the computer science students). Then some methods for solving hard problems are treated in a systematic way. For each algorithm design method, it is discussed what guarantees it can give and how we pay for the improved efficiency.|
|Lecture notes||Unterlagen und Folien werden zur Verfügung gestellt.|
|Literature||J. Hromkovic: Algorithmics for Hard Problems, Springer 2004.|
R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006.
M. Cygan et al.: Parameterized Algorithms, 2015.
F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010.
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