As machine learning models grow in size and complexity, they often become less interpretable, making it challenging to understand their decision-making processes. Explainability in machine learning addresses this issue by providing insights into how models infer outcomes, which is essential for building trust, ensuring fairness, and complying with emerging AI regulations, such as those in Europe for high-risk AI applications.

Throughout this course, you will develop a deep understanding of state-of-the-art explainability methods and learn how to integrate them effectively into your machine learning workflow. The course strikes a balance between theoretical foundations and practical applications, featuring case studies in domains like computer vision and time series forecasting. You will also explore selected libraries that facilitate the incorporation of explainability techniques into your models. Additionally, the course will guide you through the challenges and limitations of various explainability methods, helping you critically evaluate the quality and reliability of model explanations.

This online course is designed for machine learning practitioners seeking to improve the interpretability and explainability of their models. To fully benefit from this course, you should have a foundational understanding of machine learning concepts. Additionally, a basic grasp of university-level mathematics will be helpful for comprehending the theoretical underpinnings and formulas discussed.

Detecting anomalies is of high interest in multiple industries for identifying safety and security risks, ensuring production quality, or finding new business opportunities. However, anomaly detection faces some unique challenges. First, identifying anomalies by hand is difficult, especially in multidimensional data. Second, anomalies are usually poorly represented in datasets.

Anomaly detection must therefore rely largely on unsupervised learning with possibly contaminated nominal data. These methods need additional assumptions about the data to be able to identify anomalies reliably. In this online course, we will review common approaches for anomaly detection and discuss their strengths and weaknesses in different application areas.

Upon completion of the course, participants will be able to evaluate and compare the performance of diverse anomaly detection algorithms, refining their ability to discern the most effective methods for specific application areas. This comprehensive review of common approaches, empowers practitioners to make informed decisions, enhancing their proficiency in anomaly detection for various use cases.

This introductory online course provides a comprehensive overview of Artificial Intelligence (AI). You will explore the impact of AI on our lives and economy, gain an understanding of its definition, historical development, and future potential. The course covers essential concepts such as algorithms, machine learning, neural networks, and deep learning, highlighting their interconnections. It delves into potential AI applications in various fields, discussing benefits, risks, and limitations, along with current regulations and debates. You will gain insight into machine learning through supervised, unsupervised, and reinforcement learning, including deep learning with convolutional neural networks. Finally, you will learn how algorithms enable machines to learn. 

By the course's end, you will have a solid understanding of AI fundamentals and its industry impact. This beginner-friendly course caters to professionals and individuals seeking to comprehend the basics of AI, its capabilities, and how machines learn, with or without prior knowledge in the field.

Operative Nutzer_innen von KI-Anwendungen sind häufig vor Fragen gestellt, zu denen sie keine Antworten haben: Woher weiß ich, ob die KI-Anwendung fair ist? Inwieweit sollte ich bei der Entwicklung einer KI-Anwendung eingebunden sein? Wie entstehen die Entscheidungen der KI-Anwendung? 

In diesem Microlearning-Kurs erzählen wir von solchen Fragen, indem wir durch Geschichten zeigen, wie die Implementierung von KI-Anwendungen ethisch korrekt und verantwortungsvoll umgesetzt werden kann. Wir möchten diese Geschichten aus der Perspektive jener erzählen, die die KI-Anwendungen nutzen. 

Diese Geschichten dienen als Diskussionsanreger, um sich tiefer mit dem Thema menschenzentrierte KI auseinander zu setzen. Wir haben die Geschichten in der Form von Learning Nuggets verpackt; kurze, kurzweile, videobasierte Lernangebote, die flexibel angesehen und in bestehende Lehrangebote eingebettet werden können.