Search result: Catalogue data in Autumn Semester 2015

Computer Science Master Information
Focus Courses
Focus Courses in Information Systems
Focus Elective Courses Information Systems
NumberTitleTypeECTSHoursLecturers
252-0341-01LInformation Retrieval Information W4 credits2V + 1UT. Hofmann
AbstractIntroduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
ObjectiveIn depth understanding of managing, indexing, and retrieving documents with text, image and XML content. Knowledge about basic search algorithms on the web, benchmarking of search algorithms, and relevance feedback methods.
252-0373-00LMobile and Personal Information Systems Information W4 credits2V + 1UM. Norrie
AbstractThe course examines how traditional information system architectures and technologies have been adapted to support various forms of mobile and personal information systems. Topics to be covered include: databases of mobile objects; context-aware services; opportunistic information sharing; ambient information; pervasive display systems.
ObjectiveStudents will be introduced to a variety of novel information services and architectures developed for mobile environments in order to gain insight into the requirements and processes involved in designing and developing such systems and learning to think beyond traditional information systems.
ContentAdvances in mobile devices and communication technologies have led to a rapid increase in demands for various forms of mobile information systems where the users, the applications and the databases themselves may be mobile. Based on both lectures and breakout sessions, this course examines the impact of the different forms of mobility and collaboration that systems require nowadays and how these influence the design of systems at the database, the application and the user interface level. For example, traditional data management techniques have to be adapted to meet the requirements of such systems and cope with new connection, access and synchronisation issues. As mobile devices have increasingly become integrated into the users' lives and are expected to support a range of activities in different environments, applications should be context-aware, adapting functionality, information delivery and the user interfaces to the current environment and task. Various forms of software and hardware sensors may be used to determine the current context, raising interesting issues for discussion. Finally, user mobility, and the varying and intermittent connectivity that it implies, gives rise to new forms of dynamic collaboration that require lightweight, but flexible, mechanisms for information synchronisation and consistency maintenance. Here, the interplay of mobile, personal and social context will receive special attention.
263-5150-00LScientific Databases Information W4 credits2V + 1UG. H. Gonnet
AbstractScientific databases share many aspects with classical DBs, but have additional specific aspects. We will review Relational DBs, Object Oriented DBs, Knowledge DBs, textual DBs and the Semantic Web. All these topics will be studied from the point of view of the scientific applications (Bioinformatics, Physics, Chemistry, Health, Engineering) A toy SDB will be used for exercises.
ObjectiveThe goals of this course are to:
(a) Familiarize the students with how existing DBs can be used for
scientific applications.
(b) Recognize the areas where SciDBs differ and require additional
features compared to classical DBs.
(c) Be able to understand more easily SciDBs, improve existing ones
or design/create new ones.
(d) Familiarize the students with at least two examples of SciDBs.
Content1) - Introduction, Statement of the problem, course structure, exercises,
why Scientific DBs (SDBs) do not fit exactly the classical DB area.
Hierarchy: File systems, data bases, knowledge bases and variations.
Efficiency issues and how they differ from classical DB.

2) - Relational DB used for scientific data, pros/cons
Introduction to RDB, limitations of the model, basics of SQL,
handling of metadata, examples of scientific use of RDBs.

3) - Object Oriented DB. Rich/structured objects are very appealing
in SDB. OODB primitives and environments. OODB searching.
Space and access time efficiency of OODBs.

4) - Knowledge bases, key-value stores, ontologies, workflow-based
architectures. WASA.

5) - MapReduce / Hadoop

6) - Storing and sharing mathematical objects, Open Math, its relation
with OODB and Knowledge bases. Also the problem of chemical
formula representation.

7) - SGML and XML, human-readable databases, genomic databases.
Advantages of human-readable databases (the huge initial success
of genomic databases).

8) - Semantic web, Resource Description Framework (RDF) triples, SparQL.
An example of very flexible database for knowlege storage. Goals of
the Semantic Web, discussion about its future.

9) - An ideal scenario (and the design of a toy system with most of the
desired features for exploration and exercises).

10) - Automatic dependency management, (make and similar). The graph
theory problem. Critical paths.

11) - Functional testing, Verifiers, Consistency, Short-circuit testing,
Recovery and Automatic recovery, Backup (incremental) methods.

12) - Performance and space issues, various uses of compression,
concurrency control. Hardware issues, clusters, Cloud computing,
Crowd-sourcing.

13) - Guest speaker: Ioannis Xenarios (UniProtKB/Swiss-Prot).
LiteratureSeveral papers and online articles will be made available.
There is no single textbook for this course.
A significant amount of material will be delivered in the lectures making lecture attendance highly recommended.
263-5200-00LData Mining: Learning from Large Data Sets Information W4 credits2V + 1UA. Krause
AbstractMany scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications.
ObjectiveMany scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, we will study principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course will both cover theoretical foundations and practical applications.
ContentTopics covered:
- Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk)
- Fast nearest neighbor methods (Shingling, locality sensitive hashing)
- Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines)
- Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback)
- Active learning (uncertainty sampling, pool-based methods, label complexity)
- Dimension reduction (random projections, nonlinear methods)
- Data streams (Sketches, coresets, applications to online clustering)
- Recommender systems
Prerequisites / NoticePrerequisites: Solid basic knowledge in statistics, algorithms and programming. Background in machine learning is helpful but not required.
263-5210-00LProbabilistic Artificial Intelligence Information W4 credits2V + 1UA. Krause
AbstractThis course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet.
ObjectiveHow can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students.
ContentTopics covered:
- Search (BFS, DFS, A*), constraint satisfaction and optimization
- Tutorial in logic (propositional, first-order)
- Probability
- Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks)
- Probabilistic palnning (MDPs, POMPDPs)
- Reinforcement learning
- Combining logic and probability
Prerequisites / NoticeSolid basic knowledge in statistics, algorithms and programming
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