Suchergebnis: Katalogdaten im Frühjahrssemester 2019
|Vertiefung in Information Systems|
|Wahlfächer der Vertiefung in Information Systems|
|252-0312-00L||Ubiquitous Computing||W||3 KP||2V||F. Mattern, S. Mayer|
|Kurzbeschreibung||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.|
|Lernziel||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.|
|Skript||Copies of slides will be made available|
|Literatur||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
|252-3005-00L||Natural Language Understanding |
Number of participants limited to 200.
|W||4 KP||2V + 1U||M. Ciaramita, T. Hofmann|
|Kurzbeschreibung||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.|
|Lernziel||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.|
|Inhalt||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.|
|Literatur||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.|
|263-5215-00L||Fairness, Explainability, and Accountability for Machine Learning |
Number of participants limited to 40.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the course, will officially fail the course.
|W||4 KP||1V + 2P||H. Heidari|
|Lernziel||- Familiarize students with the ethical implications of applying Big Data and ML tools to socially-sensitive domains; teach them to think critically about these issues.|
- Overview the long-established philosophical, sociological, and economic literature on these subjects.
- Provide students with a tool-box of technical solutions for addressing - at least partially - the ethical and societal issues of ML and Big data.
|Inhalt||As ML continues to advance and make its way into different aspects of modern life, both the designers and users of the technology need to think seriously about its impact on individuals and society. We will study some of the ethical implications of applying ML tools to socially sensitive domains, such as employment, education, credit ledning, and criminal justice. We will discuss at length what it means for an algorithm to be fair; who should be held responsible when algorithmic decisions negatively impacts certain demographic groups or individuals; and last but not least, how algorithmic decisions can be explained to a non-technical audience. Throughout the course, we will focus on technical solutions that have been recently proposed by the ML community to tackle the above issues. We will critically discuss the advantages and shortcomings of these proposals in comparison with non-technical alternatives.|
|Voraussetzungen / Besonderes||Students are expected to sufficient knowledge of ML (i.e. they must have taken the "Introduction to Machine Learning" or an equivalent course).|
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