• 🚀 New Course Available Now!

      Methods and Issues in Explainable AI


      Ever wondered why your AI model made that specific prediction?

      Explainable AI reveals the reasoning behind AI decisions to build trust, ensure fairness, and meet regulatory requirements.

      Learn what Explainable AI is. You'll discover not only how to implement these methods, but also understand their advantages and limitations. This critical perspective will enable you to evaluate which methods are best suited for your specific use cases and requirements.

      What You'll Master:

      • Model Agnostic Post-hoc Methods including LIME, SHAP, and their variants
      • Data Valuation Methods 
      • Interpretable AI Methods for Time Series Forecasting
      • Feature Attribution in computer vision and adversarial scenarios
      • Critical evaluation of each method's strengths and limitations

      Through hands-on exercises in Python, you'll develop the expertise to make informed decisions about XAI implementation in your projects.

      ⏱️ Duration
      25 hours approximately
      divided in 5 self-paced modules
      📚 Level
      Intermediate
      Basic Python & University Math required
       Enroll Now!

      Start your journey into explainable AI today

      appliedAI Institute for Europe • Making AI explainable and trustworthy

Available courses

Methods and Issues in Explainable AI

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.

AI Essentials: Eine Einführung in die KI

Was ist KI? Und was ist Machine Learning? Und warum sind diese Themen plötzlich so wichtig?

Diese und weitere Fragen beantwortet unser Anfänger-Kurs “AI Essentials: Eine Einführung in die KI”. 

Nach dem Abschluss dieses Kurses wirst du KI im Alltag erkennen, die wichtigsten Ereignisse in ihrer Entstehungsgeschichte benennen und die Vor- und Nachteile von künstlicher Intelligenz aufzählen können. Wir zeigen dir auch die neuesten Anwendungsfälle und umreißen kurz die Entwicklungen in der KI-Regulierung.

Wenn Du Lust hast, Dich informiert an einer Diskussion auf Einstiegsniveau über KI beteiligen zu können, oder einfach verstehen möchtest, wie KI funktioniert, ist dieser Kurs genau für Dich.

TransferLab: Practical Anomaly Detection

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.

The MLOps Workbook · A Guided Online Course for Getting Started with MLOps

This course will help you and your team understand the concepts and best practices needed to scale up your Machine Learning Operations (MLOps) in your machine learning projects. It provides ML project team members with an extensive overview of the challenges and decisions encountered in building professional ML systems. Rather than focusing on evolving tools, the course emphasizes concepts and frameworks that help to share a common understanding of MLOps within ML teams. The course is structured around the ML Lifecycle, a key perspective on MLOps, from planning a machine learning project to implementing feedback loops after your project is deployed. Among other things, you will learn about how to plan an ML project, how to apply the appliedAI Project Management Framework to your ML projects, how various accountabilities should be involved during the different project phases and how the ML Principles function as the foundation of MLOps. The course comes with a video series and a workbook that you can keep to easily access the course content. You work with your own copy of the workbook in the form of a PDF file, digital whiteboard, or physical copy and work alongside the video series.

AI Essentials: A Comprehensive Introduction

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.

KI-Kompetenz für Ihr KMU – Ein Anwendungskurs

Der Onlinekurs KI-Kompetenz für Ihr KMU – Ein Anwendungskurs gibt kleine und mittlere Mittelständische Unternehmen einen Einblick darüber, welche Schritte sie tun müssen, um mit KI anzufangen. Ganz praktisch und pragmatisch wird erklärt, was die ersten Überlegungen sein sollten. Das begleitende Übungsheft enthält viele Fragen und Raum für eigene Gedanken, so dass Sie sich Gedanken zur Anwendung von KI in ihrem eigenen Unternehmen machen können. 

Verantwortungsvoller und ethisch korrekter Einsatz von Künstlicher Intelligenz

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.

 

Grundlagen der KI für Unternehmen

Die Digitalisierung und der Einsatz von KI rücken für viele Unternehmen weiter nach oben auf der Prioritätenliste, da KI das Potenzial hat, Innovation und Produktivität zu steigern.

Viele Unternehmen tun sich jedoch noch schwer mit dem Einsatz von KI, weil nicht klar ist, was mit KI tatsächlich möglich ist. Manche Mitarbeiter haben bedenken hinsichtlich des "richtigen" Einsatzes von KI und ihrer Auswirkungen auf den Arbeitsplatz bestehen oder es mangelt an den nötigen KI-Fähigkeiten und -Wissen im Unternehmen.

Ziel dieses Kurses ist es, die Teilnehmer zu befähigen, ihr Unternehmen ganzheitlich und praxisnah auf die Einführung von KI vorzubereiten. Für Unternehmen, die bereits an KI interessiert sind, soll dieser Kurs ein erster Schritt zum Einstieg sein.

Dieser Kurs ermöglicht einen einfachen Einstieg und setzt keine Vorkenntnisse über KI voraus. Absolventen dieses Kurses haben ein solides Verständnis über die Möglichkeiten von KI in Unternehmen, die Auswirkungen auf den Arbeitsplatz und sind somit gut vorbereitet, um den Einsatz von KI in ihrer Organisation voranzutreiben.

Foundations of AI: For Business

Digitization and the use of AI are moving higher up the priority list for many companies, as AI has the potential to increase innovation and productivity.

However, many companies are still struggling with the use of AI because it is not clear what is actually possible with AI. Some employees have concerns about the "right" use of AI and its impact on the workplace, or there is a lack of necessary AI skills and knowledge in the company.

The goal of this course is to enable participants to prepare their company for the introduction of AI in a holistic and practical way. For companies already interested in AI, this course is intended to be a first step to getting started.

This course provides an easy entry point and assumes no prior knowledge of AI. Graduates of this course will have a solid understanding of the possibilities of AI in business, the impact on the workplace, and will therefore be well prepared to drive the use of AI in their organization.