Search result: Catalogue data in Spring Semester 2019

Computer Science TC Information
Detailed information on the programme at: Link
Specialized Courses in Respective Subject with Educational Focus
NumberTitleTypeECTSHoursLecturers
272-0300-00LAlgorithmics for Hard Problems Information
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A.
W4 credits2V + 1UH.‑J. Böckenhauer, R. Kralovic
AbstractThis course unit looks into algorithmic approaches to the solving of hard problems, particularly with moderately exponential-time algorithms and parameterized algorithms.

The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools.
ObjectiveTo systematically acquire an overview of the methods for solving hard problems. To get deeper knowledge of exact and parameterized algorithms.
ContentFirst, 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. A special focus lies on moderately exponential-time algorithms and parameterized algorithms.
Lecture notesUnterlagen und Folien werden zur Verfügung gestellt.
LiteratureJ. 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.
272-0301-00LMethods for Design of Random Systems Information
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 B.
W4 credits2V + 1U
AbstractThe students should get a deep understanding of the notion of randomness and its usefulness. Using basic elements probability theory and number theory the students will discover randomness as a source of efficiency in algorithmic. The goal is to teach the paradigms of design of randomized algorithms.
ObjectiveTo understand the computational power of randomness and to learn the basic
methods for designing randomized algorithms
Lecture notesJ. Hromkovic: Randomisierte Algorithmen, Teubner 2004.

J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006.

J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
LiteratureJ. Hromkovic: Randomisierte Algorithmen, Teubner 2004.

J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006.

J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
272-0302-00LApproximation and Online Algorithms Information W4 credits2V + 1UH.‑J. Böckenhauer, D. Komm
AbstractThis lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches.
ObjectiveGet a systematic overview of different methods for designing approximative algorithms for hard optimization problems and online problems. Get to know methods for showing the limitations of these approaches.
ContentApproximation algorithms are one of the most succesful techniques to attack hard optimization problems. Here, we study the so-called approximation ratio, i.e., the ratio of the cost of the computed approximating solution and an optimal one (which is not computable efficiently).
For an online problem, the whole instance is not known in advance, but it arrives pieceweise and for every such piece a corresponding part of the definite output must be given. The quality of an algorithm for such an online problem is measured by the competitive ratio, i.e., the ratio of the cost of the computed solution and the cost of an optimal solution that could be given if the whole input was known in advance.

The contents of this lecture are
- the classification of optimization problems by the reachable approximation ratio,
- systematic methods to design approximation algorithms (e.g., greedy strategies, dynamic programming, linear programming relaxation),
- methods to show non-approximability,
- classic online problem like paging or scheduling problems and corresponding algorithms,
- randomized online algorithms,
- the design and analysis principles for online algorithms, and
- limits of the competitive ratio and the advice complexity as a way to do a deeper analysis of the complexity of online problems.
LiteratureThe lecture is based on the following books:

J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004

D. Komm: An Introduction to Online Computation: Determinism, Randomization, Advice, Springer, 2016

Additional literature:

A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998
272-0400-00LMentored Work Specialised Courses in the Respective Subject with Educational Focus Computer Sc A Information Restricted registration - show details W+2 credits4AJ. Hromkovic, G. Serafini
AbstractIn the mentored work on their subject specialisation, students link high-school and university aspects of the subject, thus strengthening their teaching competence with regard to curriculum decisions and the future development of the tuition. They compile texts under supervision that are directly comprehensible to the targeted readers - generally specialist-subject teachers at high-school level.
ObjectiveThe aim is for the students
- to familiarise themselves with a new topic by obtaining material and studying the sources, so that they can selectively extend their specialist competence in this way.
- to independently develop a text on the topic, with special focus on its mathematical comprehensibility in respect of the level of knowledge of the targeted readership.
- To try out different options for specialist further training in their profession.
ContentThematische Schwerpunkte:
Die mentorierte Arbeit in FV besteht in der Regel in einer Literaturarbeit über ein Thema, das einen Bezug zum gymnasialem Unterricht oder seiner Weiterentwicklung hat. Die Studierenden setzen darin Erkenntnisse aus den Vorlesungen in FV praktisch um.

Lernformen:
Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte
Arbeit ist Teil des Portfolios der Studierenden.
Lecture notesEine Anleitung zur mentorierten Arbeit in FV wird zur Verfügung gestellt.
LiteratureDie Literatur ist themenspezifisch. Sie muss je nach Situation selber beschafft werden oder wird zur Verfügung gestellt.
Prerequisites / NoticeDie Arbeit sollte vor Beginn des Praktikums abgeschlossen werden.
252-0408-00LCryptographic Protocols Information W5 credits2V + 2UM. Hirt, U. Maurer
AbstractThe 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.
ObjectiveIndroduction to a very active research area with many gems and paradoxical
results. Spark interest in fundamental problems.
ContentThe 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 notesthe 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 / NoticeA 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.
263-2300-00LHow To Write Fast Numerical Code Information Restricted registration - show details
Number of participants limited to 84.

Prerequisite: Master student, solid C programming skills.

Takes place the last time in this form.
W6 credits3V + 2UM. Püschel
AbstractThis course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality such as matrix operations, transforms, and others. The focus is on optimizing for a single core. This includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.
ObjectiveSoftware performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project.
ContentThe 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 mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing.

Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course.
252-0341-01LInformation Retrieval Information W4 credits2V + 1UG. Fourny
AbstractThis course gives an introduction to information retrieval with a focus on text documents and unstructured data.

Main topics comprise document modelling, various retrieval techniques, indexing techniques, query frameworks, optimization, evaluation and feedback.
ObjectiveWe keep accumulating data at an unprecedented pace, much faster than we can process it. While Big Data techniques contribute solutions accounting for structured or semi-structured shapes such as tables, trees, graphs and cubes, the study of unstructured data is a field of its own: Information Retrieval.

After this course, you will have in-depth understanding of broadly established techniques in order to model, index and query unstructured data (aka, text), including the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision.

You will know how to make queries faster and how to make queries work on very large datasets. You will be capable of evaluating the quality of an information retrieval engine.

Finally, you will also have knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank.
Content1. Introduction

2. Boolean retrieval: the basics of how to index and query unstructured data.

3. Term vocabulary: pre-processing the data prior to indexing: building the term vocabulary, posting lists.

4. Tolerant retrieval: dealing with spelling errors: tolerant retrieval.

5. Index construction: scaling up to large datasets.

6. Index compression: how to improve performance by compressing the index in various ways.

7. Ranked retrieval: how to ranking results with scores and the vector space model

8. Scoring in a bigger picture: taking ranked retrieval to the next level with various improvements, including inexact retrieval

9. Probabilistic information retrieval: how to leverage Bayesian techniques to build an alternate, probabilistic model for information retrieval

10. Language models: another alternate model based on languages, automata and document generation

11. Evaluation: precision, recall and various other measurements of quality

12. Web search: PageRank

13. Wrap-up.

The lecture structure will follow the pedagogical approach of the book (see material).

The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture.
LiteratureC. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press.
Prerequisites / NoticePrior knowledge in elementary set theory, logics, linear algebra, data structures, abstract data types, algorithms, and probability theory (at the Bachelor's level) is required, as well as programming skills (we will use Python).
252-1403-00LInvitation to Quantum Informatics Information W3 credits2VS. Wolf
AbstractFollowed 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.
ObjectiveIt 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.
ContentAccording 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.
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