Schwerpunkt: AI

From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.

The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
• Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
• Regression (Linear Regression, Logistic Regression)
• Clustering (k-means)
• Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.

This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.

This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.

We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.

In the summer of 2020, a series of antitrust hearings in the US—involving some of the world’s leading tech companies—highlighted the double-edged nature of many emerging digital technologies, including artificial intelligence (AI), the Internet of Things (IoT), blockchain and big data. While advanced digital technologies are often linked to exciting opportunities for organizational transformation and societal growth, we are beginning to realize that these technologies also come with potentially undesirable impacts. The hearings covered a range of contentious topics, including the regulation of free speech, data privacy and AI bias, emphasizing the fact that the ability to identify, analyze, and potentially mitigate ethical tensions in a digital context is a key skill in the digital economy we are currently building. Indeed, as humans weave modern digital technologies so tightly into the fabric of everyday life, enhancing their capabilities with cutting edge technologies, the need for scrutiny and safeguards becomes paramount if progress in not only to be driven by what is technologically possible, but by what is societally desirable and sustainable.

One way for organizations to minimize the ethical risks associated with new digital technologies is to put in place policies that encourage a responsible approach to their development, use and modification. While such policies should be considered as part of an organization’s larger corporate responsibility, this seminar will make a case that the history and nature of digital technologies warrant an explicit and—to some degree—separate consideration of digital ethics in business and society.

Topics we will discuss during this seminar include:
> Historical and conceptual roots of digital ethics
> Digital issues in the context of corporate responsibility
> Foundational frameworks of corporate digital responsibility
> Design and implementation of corporate digital responsibility

Overall, the seminar is designed to allow you to understand key concepts that currently shape discussions of digital ethics while building up the skills needed to apply these concepts to specific contexts, cases, and challenges you will face.

Completing this course will support you in developing the following skills:
> Explain sources of ethical issues in a digital context
> Anticipate and analyze ethical issues in a digital context
> Understand foundational frameworks of corporate digital responsibility
> Apply foundational frameworks to industry cases

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.