Search result: Catalogue data in Spring Semester 2018

CAS in Computer Science Information
Focus Courses and Electives
NumberTitleTypeECTSHoursLecturers
263-4312-00LAdvanced Data Structures Information W5 credits2V + 2UP. Uznanski
AbstractData 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.
ObjectiveLearning 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.
ContentThis 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)
- hashing
- dynamic graphs connectivity
Prerequisites / NoticeThis is a highly theoretical course. You should be comfortable with:
- algorithms and data structures
- probability

Completing Algorithms, Probability, and Computing course (252-0209-00L) is a good indicator.
263-4600-00LFormal Methods for Information Security Information W4 credits2V + 1UR. Sasse, C. Sprenger
AbstractThe 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.
ObjectiveThe 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.
ContentThe 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.
272-0300-00LAlgorithmics for Hard Problems 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 A.
W4 credits2V + 1UJ. Hromkovic
AbstractThis 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.
ObjectiveTo systematically acquire an overview of the methods for solving hard problems.
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.
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
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 + 1UH.‑J. Böckenhauer, D. Komm, R. Kralovic
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
401-3052-05LGraph Theory Information W5 credits2V + 1UB. Sudakov
AbstractBasic notions, trees, spanning trees, Caley's formula, vertex and edge connectivity, blocks, 2-connectivity, Mader's theorem, Menger's theorem, Eulerian graphs, Hamilton cycles, Dirac's theorem, matchings, theorems of Hall, König and Tutte, planar graphs, Euler's formula, basic non-planar graphs, graph colorings, greedy colorings, Brooks' theorem, 5-colorings of planar graphs
ObjectiveThe students will get an overview over the most fundamental questions concerning graph theory. We expect them to understand the proof techniques and to use them autonomously on related problems.
Lecture notesLecture will be only at the blackboard.
LiteratureWest, D.: "Introduction to Graph Theory"
Diestel, R.: "Graph Theory"

Further literature links will be provided in the lecture.
Prerequisites / NoticeNOTICE: This course unit was previously offered as 252-1408-00L Graphs and Algorithms.
401-3632-00LComputational StatisticsW10 credits3V + 2UM. H. Maathuis
AbstractComputational Statistics deals with modern statistical methods of data analysis (aka "data science") for prediction and inference. The course provides an overview of existing methods. The course is hands-on, and methods are applied using the statistical programming language R.
ObjectiveIn this class, the student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R.
ContentSee the class website
Prerequisites / NoticeAt least one semester of (basic) probability and statistics.

Programming experience is helpful but not required.
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