The course provides an introduction to Information Architecture

• Introduction to Information Architecture
• Introduction to Relevance Theory
• Introduction to Multimodality
• Introduction to NLP/Comp. Ling.
• Introduction to Ontology Engineering

Einführung in die Wahrscheinlichkeitstheorie

Verwendbarkeit des Moduls:

Pflichtveranstaltung im Bachelorstudiengang Wirtschaftsmathematik (B.Sc.)

Wahlpflichtveranstaltung im Studiengang Lehramt Mathematik an Gymnasien

Lernziele/ Kompetenzen:

In dieser Vorlesung werden die wichtigsten Grundbegriffe, Resultate und Rechentechniken der

Wahrscheinlichkeitstheorie dargestellt. Die Betonung der Vorlesung liegt auf den Konzepten, die

mit vielen Beispielen illustriert werden, und nicht auf den mathematisch-technischen Aspekten

(die z.T. recht aufwändig sind). Daher wendet sich diese Vorlesung nicht nur an Mathematiker

(im Grundstudium), sondern auch an alle anderen Studenten, die Mathematik im Nebenfach hören

oder aus anderen Gründen an Wahrscheinlichkeitstheorie interessiert sind.

Inhalt in Stichworten:

Wahrscheinlichkeitsraum, bedingte Wahrscheinlichkeit, Bayessche Formeln, Zufallsvariablen,

Verteilungen, Lebesgueintegration, Erwartungswert, Varianz, Kovarianz, Unabhängigkeit,

Konvergenzbegriffe, Gesetze der grossen Zahlen, zentraler Grenzwertsatz, Satz von Poisson,

bedingte Erwartung, Methode der kleinsten mittleren Fehlerquadrate, stochastische Prozesse,

Markovketten.

Voraussetzungen und Vorkenntnisse

Analysis I + II, Lineare Algebra I

Studien- und Prüfungsleistungen:

Leistungen:     Übungsblätter

Prüfungsform:   Schriftliche Prüfung (90 Min.)

siehe Vorlesung EWT

siehe Vorlesung EWT

In this course we develop the theory of modeling financial asset prices and corresponding trading strategies in continuous time. Applications include the pricing and hedging of financial derivatives and the construction of optimal investment strategies. In particular we will derive the celebrated formulas of Bachelier and of Black, Scholes, and Merton. 

Our approach will differ from the usual approach found in most textbooks in that it will be based on a strictly pathwise version of Itô calculus. Thus we can avoid the technically demanding theory of stochastic integration. As a consequence, prior knowledge in Probability Theory is not essential (although it may be helpful). Very good skills in Analysis I & II are required. 

Lecture notes will be made available to all participants. 

Lehrinhalte:

This course gives a brief introduction to the fundamentals of human-computer interaction (HCI). Subsequently, different aspects and research methods in HCI will be considered that are needed to design,
conduct, and report a user study. In detail, the students will gain theoretical knowledge about:

* identifying a research hypothesis,
* specifying the design of a study (conditions, environments, tasks,
etc.),
* selecting appropriate means of measures (quantitative, qualitative),
* designing questionnaires, and
* analyzing and reporting the results.

Besides gaining theoretical knowledge on HCI methods, the students will be empowered applying these methods. To this end, they will design a user study in a small group of fellow students. This group will actually run the data collection sessions of the study, analyze and report the results, and provide some conclusions. To this end, the user study will be documented in writing and video. Optionally, the course will
introduce into further topics such as surveys, diaries, case studies, interviews, or focus groups.

Begleitende Literatur:

- Lazar, Feng, Hochheiser: Research Methods in Human-Computer Interaction, Wiley, 2010.
- Field and Hole: How to Design and Report Experiments, Sage, 2003.

Teamproject over two semesters (HWS 2013 und FSS 2014) für students of the M. Sc. Business Informatics. The exact dates and times will be determined together with the students.

6 ECTS in the HWS and 6 ECTS in the FSS.

Kontakt für Rückfragen: Philip Mildner (mildner@pi4.informatik.uni-mannheim.de)

1. Introduction – Motivation for networks, history; protocol hierarchies; standardization bodies; the ISO
Reference Model for Open Systems Interconnection

2. The Physical Layer – Definition; mechanical/electrical/functional properties of layer 1; transmission
techniques; modulation techniques; bit encoding;physical media; example: ADSL

3. Data Link Layer – Transmission errors: causes, detection, correction; error detecting and error correcting
codes; multiplexing; sequence numbers and acknowledgments; flow control; example: PPP

4. Local Area Networks – Topologies for LANs; medium access control: ALOHA, CSMA/CD (Ethernet); hubs,
switches and bridges

5. Wide Area Networks and Routing – Packet switching vs. circuit switching; virtual circuits vs. datagrams;
addressing in WANs; routing algorithms for point-topoint traffic; routing algorithms for multicast traffic;
example: IPv4

6. Transport Layer – Purpose of the transport layer; transport protocols in the Internet: UDP; TCP,
congestion control in TCP; RTP

7. Application Layer Protocols – smtp for electronic mail; ftp for file transfer; nfs for remote file access; telnet
for remote login; http for Web access

8. The Domain Name System – DNS architecture, DNS protocols

Übung zur gleichnamigen Vorlesung.

Der Termin kann auf Wunsch verlegt werden.

1. Introduction to computer graphics
2. Efficient algorithms to draw lines, ellipses, polygons
3. Image processing (remove noise, adapt contrast, detect edges, seam carving, high dynamic range
images)
4. Content analysis of images
5. Algorithms for video preocessing and analysis
6. Applications (Robocup, Grand Challenge, new research topics)

Der Termin kann auf Wunsch verlegt werden.

The course introduces the fundamental concepts of computer graphics. Topics cover the representation of lights and colors; ray tracing and high dynamic range (HDR); and applications like computer games, animations in movies, or virtual reality.

Topics

·       Representation of Lights and Colors

·       Image Input and Output Devices (Camera, Display)

·       Graphics Processing Units (GPUs)

·       Fast Algorithms to Draw Lines, Circles, and Ellipses

·       Drawing Primitives in OpenGL

·       Image Transformations 

·       Sampling, Aliasing, and Antialiasing

·       Reflection and Illumination

·       Modeling and Rendering

·       Ray Tracing

·       High Dynamic Range (HDR)

·       Applications of Computer Graphics (Animations in Movies, Computer Games, Virtual Reality, CAD, Simulations)

Registration: No registration via the Studienportal. Simply visit the first lesson on septembre 4th.

Please visit our website for further information.

Please note that on October, 7 th the ASE lecture will take place in room EO 145 (Schloss Ehrenhof Ost).

The final decision concerning the rooms will be made after this lecture.

Please note that the tutorial will take place in room C 015 again.

Die erste Übung findet am 12.09.2013 statt.

First tutorial: Sept. 12 th, 2013.

Die erste Übung findet am 12.09.2013 statt.

Raum: A 5, 6 - C 013

First tutorial: Sept. 12 th, 2013.

Room: A 5, 6 - C 013

In the future, robots will play an increasingly important role in many areas of human society from domestic housekeeping and geriatric care to manufacturing and running businesses. To best exploit these new opportunities, new human-friendly approaches for describing desired robot behavior are needed. The goal of this team project is to develop a user-friendly language and modeling environment to support the quick, simple and reliable description of new robot applications. The project will revolve around the design and implementation of a new domain-specific language for modeling robot behavior using the multi-level modeling approach under development in the software engineering group.

Required skills: A passion for programming, modeling and robots

Participants: 3-5 students

Language: English/German

Contact: Ralph Gerbig

The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:

• Goals and Principles of Data Mining
• Data Representation and Preprocessing
• Clustering
• Classification
• Association Analysis
• Sequential Patterns
• Text Mining
• Systems and Applications (e.g. Retail, Finance, Web Analysis)

The course consists of a lecture together with accompanying practical exercises as well as student team projects. In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets. The team projects take place in the last third of the term. Within the projects, students realize more sophisticated data mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.

Please see the course homepage for more information (http://dws.informatik.uni-mannheim.de/en/teaching/courses-for-master-candidates/bi-600-data-mining/)

Das Doktoranden- und Diplomanden Kolloquium des Lehrstuhls dient der Besprechung von Dissertationsthemen, laufenden und abgeschlossenen Diplom/Bachelorarbeiten, und ausgewählten Studienarbeiten. Bisweilen wird das Programm durch Gastvorträge ergänzt. An den jeweiligen Terminen werden ein bis zwei Arbeiten präsentiert und diskutiert.

Eine Teilnahme an dieser Veranstaltung ist jederzeit bei Interesse an den jeweiligen Themen möglich. Insbesondere in Vorbereitung auf die eigene Diplomarbeit lohnt sich ein Besuch der Veranstaltung, um so einen Eindrücke zu Diplomarbeitsthemen und möglichen Herangehensweisen zu erhalten.

Die Veranstaltung findet in B6, 26 Raum A 2.06 statt.

Data integration is one of the key challenges within most IT projects. Within the enterprise context, data integration problems arise whenever data from separate sources needs to be combined as the basis for new applications. Within the context of the Web, data integration techniques are the key for taking advantage of the ever growing number of publicly-accessible data sources and for enabling applications such as product comparison portals, location-based mashups, and entity search engines.  In the course, students will learn techniques for integrating and cleansing data from large sets of data sources. The course will cover the following topics:

• Heterogeneity and Distributedness
• The Data Integration Process
• Web Data Formats
• Schema Matching and Data Translation
• Identity Resolution
• Data Quality Assessment
• Data Fusion

 The course consists of a lecture together with accompanying practical exercises.  In the exercises the participants will gather expertise in applying state of the art data integration techniques along the case study of a real-world Web data integration project. Students will work on their projects in teams and will report about the results of their projects in the form of a written report as well as an oral presentation. 

Prerequisites:  Java programming skills

Data integration is one of the key challenges within most IT projects. Within the enterprise context, data integration problems arise whenever data from separate sources needs to be combined as the basis for new applications. Within the context of the Web, data integration techniques are the key for taking advantage of the ever growing number of publicly-accessible data sources and for enabling applications such as product comparison portals, location-based mashups, and entity search engines.  In the course, students will learn techniques for integrating and cleansing data from large sets of data sources. The course will cover the following topics:

• Heterogeneity and Distributedness
• The Data Integration Process
• Web Data Formats
• Schema Matching and Data Translation
• Identity Resolution
• Data Quality Assessment
• Data Fusion

The course consists of a lecture together with accompanying practical exercises.  In the exercises the participants will gather expertise in applying state of the art data integration techniques along the case study of a real-world Web data integration project. Students will work on their projects in teams and will report about the results of their projects in the form of a written report as well as an oral presentation.

The goal of this team project is the creation of a large scale knowledge base from data scattered on HTML pages, in particular tables, all over the World Wide Web. Participants will gather hands on experience with one of the largest data sets available, the Common Crawl, and address typical Big Data challenges such as data semantics, data interlinking, and data quality

• Pre-requisites: Databases, Java programming, User-interface design
• Nähere Informationen finden Sie auf der Homepage des Lehrstuhls.

Starting from October 1, 2013 the tutorial has been shifted to Tuesdays, 12:00-13:30 in room A2.06.

The term "Semantic Web" was coined in 2001 when Tim Berners Lee (the inventor of the World Wide Web) and others presented their vision of an intelligent web in the "Scientific American". The Semantic Web aims at the development of methods that help to automate the interpretation, aggregation, evaluation and comparison of information on the Web.

This course gives an introduction to the technical foundations of Semantic Web Technologies, including knowledge representation and query languages, as well as logical inference. More specifically, it covers the following contents:

* Vision and Principles of the Semantic Web
* Representation Languages (XML, RDF, RDF Schema, OWL)
* Knowledge Modeling: Ontologies and Linked Data
* Logical Reasoning in RDF and OWL
* Commercial and Open Source Tools and Systems

Learning outcomes:

In a team project, the students will learn how to design and implement Semantic Web applications. They will become familiar with standardized modeling languages for building knowledge representations, and see how to query these models by means of languages such as SPARQL. After taking this course, the students will be aware of the problems and benefits of semantic technologies in the context of tasks such as knowledge management, information search and data integration, and capable of judging the applicability of these technologies for addressing practical challenges.

Requirements:

* Java programming skills
* preferably, some experience with software development

Literature:

Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American, 284 (5), pp. 34-43, 2001
Pascal Hitzler, Markus Krötzsch and Sebastian Rudolph. Foundations of Semantic Web Technologies. Chapman & Hall/CRC, 2009
oder: Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph and York Sure. Semantic Web: Grundlagen. Springer, 2007

Raum: B6 A2.06

Statistics and Artificial Intelligence (AI) are highly intertwined, since many AI problems can be formulated as problems of statistical inference, and statistical methods, in turn, represent de-facto the standard way to solve many, if not the majority, of AI problems. This class will provide an advanced overview of state-of-the-art principles and methods of AI. The main focus will be on probabilistic reasoning techniques, statistical models, and their application to a wide variety of problems in Natural Language Processing (NLP) and Information Retrieval (IR). Covered topics will include:

• Probabilistic Reasoning - Bayesian and Markov networks:

  • exact and approximate inference methods;

  • sampling algorithms;

  • parameter estimation.

• Statistical Methods for NLP:

  • language modeling;

  • topic modeling;

  • Machine Translation.

• Statistical Methods for IR:

  • random walk algorithms for ranking;

  • language models for ranking;

  • learning to rank.

Coursework will include homework assignments, a term project and a final exam. Homework assignments are meant to introduce the students to the problems that will be covered in the final exam at the end of the course. In addition, students are expected to successfully complete a term project in teams of 2-4 people. The projects will focus on a variety of AI problems covered in class. Project deliverables include both software (i.e., code and documentation) and a short report explaining the work performed and its evaluation.

Course level: Master and Diploma; Course language: English;

Prerequisites: Basic programming skills; Basic knowledge of AI and probability theory.

Statistics and Artificial Intelligence (AI) are highly intertwined, since many AI problems can be formulated as problems of statistical inference, and statistical methods, in turn, represent de-facto the standard way to solve many, if not the majority, of AI problems. This class will provide an advanced overview of state-of-the-art principles and methods of AI. The main focus will be on probabilistic reasoning techniques, statistical models, and their application to a wide variety of problems in Natural Language Processing (NLP) and Information Retrieval (IR). Covered topics will include:

• Probabilistic Reasoning - Bayesian and Markov networks:

  • exact and approximate inference methods;

  • sampling algorithms;

  • parameter estimation.

• Statistical Methods for NLP:

  • language modeling;

  • topic modeling;

  • Machine Translation.

• Statistical Methods for IR:

  • random walk algorithms for ranking;

  • language models for ranking;

  • learning to rank.

Coursework will include homework assignments, a term project and a final exam. Homework assignments are meant to introduce the students to the problems that will be covered in the final exam at the end of the course. In addition, students are expected to successfully complete a term project in teams of 2-4 people. The projects will focus on a variety of AI problems covered in class. Project deliverables include both software (i.e., code and documentation) and a short report explaining the work performed and its evaluation.

Course level: Master and Diploma; Course language: English;

Prerequisites: Basic programming skills; Basic knowledge of AI and probability theory.