| International Conference: Relevance of Global Commodity Chains for Risk Assessment | The conference will bring together national and international experts in feed and food chains, digitalisation and consumer health protection to exchange and transfer knowledge on concepts, data and tools for a holistic evaluation of risks in global commodity chains. | German Federal Institute for Risk Assessment (BfR-Akademie) | https://www.bfr-akademie.de/english/gwk2024.html | |
| BDTI Essentials Course | This free course aims to help public administrations explore the free-of-charge data analytics playground the Big Data Test Infrastructure (BDTI) offers through a practical use case. After this course, you will be ready to apply for BDTI and build a public sector data use case using the platform. | European Commission | https://big-data-test-infrastructure.ec.europa.eu/resources/courses-and-training/bdti-essentials-course_en | |
| Workshop: „Speech2Summary: Vortrainierte KI-Modelle” | Im Workshop 'Speech2Summary' am Hasso-Plattner-Institut lernen Teilnehmer, Audiodateien mit vortrainierten KI-Modellen in Text umzuwandeln und zu summieren, wobei Methoden zur Modellbewertung und -bereitstellung erörtert werden. | KI-Servicezentrum Berlin-Brandenburg by Hasso-Plattner-Institut | https://hpi.de/kisz/events/2024/workshop-speech2summary-vortrainierte-ki-modelle.html | |
| Workshop: "Containern Sie Ihre Intelligenz" | Der kostenlose Workshop am 06.02.2024 richtet sich an KI-Enthusiasten und Entwickler ohne Vorkenntnisse in FastAPI oder Docker. Er bietet praktische Übungen zur Bereitstellung von KI-Modellen mittels Docker und FastAPI, um trainierte KI-Modelle in einsatzfähige Lösungen zu überführen. | KI-Servicezentrum Berlin-Brandenburg by Hasso-Plattner-Institut | https://hpi.de/kisz/events/2024/workshop-containern-sie-ihre-intelligenz-1.html | |
| KI-Workshop: "Effizientes Abfragen von Informationen aus Dokumenten - Lokales Retrieval Augmented Generation-System" | Der kostenlose KI-Workshop am 07.02.2024, lehrt Teilnehmer, ein lokales End-to-End-System mit Python und Mathematik-Kenntnissen zu implementieren. Der Fokus liegt auf dem Einsatz von Embeddings, Vektordatenbanken und lokalen Large Language Models für das Retrieval Augmented Generation-System. | KI-Servicezentrum Berlin-Brandenburg by Hasso-Plattner-Institut | https://hpi.de/kisz/events/2024/ki-workshop-effizientes-abfragen-von-informationen-aus-dokumenten.html | |
| KI-Workshop: „Arbeiten mit Embeddings: Ein Einführungsworkshop mit Anwendungen zur semantischen Suche“ | Im kostenlosen KI-Workshop 'Arbeiten mit Embeddings' am HPI am 14.02.2024 lernen Teilnehmer mit Python-Grundkenntnissen, wie man semantische Suchmaschinen mittels verschiedener Embedding-Modelle erstellt. Der Workshop umfasst die Erkundung von Embedding-Techniken und deren praktische Anwendung in der semantischen Suche. | KI-Servicezentrum Berlin-Brandenburg by Hasso-Plattner-Institut | https://hpi.de/kisz/events/2024/ki-workshop-arbeiten-mit-embeddings-ein-einfuehrungsworkshop-mit-anwendungen-z-1.html | |
| Elements of AI | Der Online-Kurs Elements of AI bietet leicht verständliche KI-Grundlagen mit praktischen Übungen. Keine Vorkenntnisse nötig. Entwickelt von Reaktor und der Universität Helsinki, wird die deutsche Version von DIHK-Bildungs-GmbH und dem BMWi unterstützt. | University of Helsinki, Reaktor Education | http://elementsofai.de/ | |
| Künstliche Intelligenz und maschinelles Lernen für Einsteiger | Der openHPI-Kurs für Einsteiger demystifiziert KI und maschinelles Lernen. Zielgruppe sind Laien. Kursleiter Hötter und Warmuth erklären die Grundlagen und verschiedene Lernmethoden anhand von Beispielen. Der Kurs endet mit einem Blick auf ethische und technische Grenzen. Python-Programmierung ist nicht Teil des Kurses. | OpenHPI | https://open.hpi.de/courses/kieinstieg2020 | |
KI und Datenqualität - Perspektiven aus Data Science, Ethik, Normung und Recht
| Künstliche Intelligenz, besonders neuronale Netze, benötigt qualitativ hochwertige Trainingsdaten. Die Datenqualität umfasst informatische, juristische und ethische Aspekte. Fehlende oder fehlerhafte Daten können zu unsicheren KI-Modellen führen. Der Kurs "KI und Datenqualität" behandelt diese Themen im Kontext des KITQAR Projekts. | OpenHPI | https://open.hpi.de/courses/kidaten2023 | |
| Foundations of Artificial Intelligence I | The course "Foundations of Artificial Intelligence I" delves into the concept of the rational agent in AI. It begins with Alan Turing's inquiry into machine intelligence, explores symbolic and subsymbolic AI, and reviews the Turing test. The course also examines agent properties, environments, and various agent architectures. | KI-Campus | https://ki-campus.org/courses/foundationsai-dfki2021?language_content_entity=en | |
| Foundations of Artificial Intelligence II | The course "Foundations of Artificial Intelligence II" covers key search algorithms in AI, blending theory with practical examples. It is structured into three modules focusing on systematic search algorithms, heuristic search, and local and stochastic search methods. | KI-Campus | https://ki-campus.org/courses/foundationsofai-II-dfki2021?language_content_entity=en | |
| Foundations of Artificial Intelligence III | The course "Foundations of Artificial Intelligence III" delves into mathematical logic and satisfiability checking, covering propositional and first-order logic, their syntax, semantics, and related algorithms like the DPLL. It emphasizes the use of modern Satisfiability (SAT) solvers in AI and sets the theoretical groundwork for subsequent courses on Knowledge Representation and Constraint Optimization. | KI-Campus | https://ki-campus.org/courses/foundationsofai-III-dfki2021?language_content_entity=en | |
| Foundations of Artificial Intelligence IV | The "Foundations of Artificial Intelligence IV" course delves into conceptual knowledge representation using ontologies, particularly with languages like OWL rooted in AI's description logics. It covers knowledge types, the history of AI knowledge representation, and specifics like ALC logic and web ontologies. The course wraps up discussing challenges in non-monotonic reasoning and handling exceptions. | KI-Campus | https://ki-campus.org/courses/foundationsofai-IV-dfki2021?language_content_entity=en | |
| Foundations of Artificial Intelligence V | The "Foundations of Artificial Intelligence V" course explores constraint programming, emphasizing the fusion of CP and SAT in efficient CP-SAT solvers. It showcases problem modeling with MiniZinc and integrates knowledge from prior courses, supplemented with examples. | KI-Campus | https://ki-campus.org/courses/foundationsofai-V-dfki2021?language_content_entity=en | |
Daten- und Algorithmenethik
| Grundlagen der Daten- und Algorithmenethik, Überblick über kulturell geprägte Moraltheorien, aktuelle KI-Anwendungen und ethische Ansätze, sowie integrierte Ethik-Spiele zur Veranschaulichung. | KI-Campus | https://ki-campus.org/courses/daethik | |
| Data Literacy – Daten interpretieren durch Data Mining | Data Mining nutzt große Datenmengen, um Zusammenhänge zu erkennen und Vorhersagen zu treffen. Der TH Köln Kurs führt in selbstlernende Algorithmen ein, die in Jupyter Notebooks an echten Daten getestet werden können. Dabei werden alle Schritte eines Projekts, wichtige Algorithmen und deren Grenzen, sowohl fachlich als auch ethisch, behandelt. | KI-Campus / TH Köln | https://ki-campus.org/courses/dali-datamining-THK2021 | |
| Natural Language Processing | The "Natural Language Processing" course by the German Research Center for Artificial Intelligence and Technische Universität Berlin delves into machine learning-based language processing. It covers basic NLP concepts, practical tasks like text classification, and development using Python. The course also functions as a self-study MOOC for universities. | KI-Campus / Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) | https://ki-campus.org/node/487 | |
AutoML - Automated Machine Learning
| The "Automated Machine Learning" course teaches future ML developers to efficiently design ML pipelines, including hyperparameters and neural network architectures. It can be taken as a MOOC or in a blended learning format by universities. | KI-Campus | https://ki-campus.org/courses/automl-luh2021?language_content_entity=en | |
| Künstliche Intelligenz und Maschinelles Lernen in der Praxis | Nach dem Grundkurs zu KI und Maschinellem Lernen fokussiert der Folgekurs auf die praktische Anwendung realer Datenprojekte unter Leitung von Johannes Hötter und Christian Warmuth. Es werden alle Schritte eines datengetriebenen Projekts behandelt, mit vier Wochenprojekten wie Wohnungspreisvorhersage und Gebärdensprache-Erkennung. Vorwissen aus dem Einstiegskurs ist erforderlich. | OpenHPI | https://open.hpi.de/courses/kipraxis2021 | |
| Building Visual Machine Learning Models | If you seek hands-on Machine Learning experience without programming, this course is for you. Dive into building ML models using a user-friendly graphical tool, understand model mechanics, and grasp the ML project lifecycle. Optionally, transition your knowledge to Python programming by the course's end. | KI Campus / DHBW Stuttgart | Building Visual Machine Learning Models | KI-Campus | |
| AMALEA – Angewandte Machine-Learning-Algorithmen | Der Kurs bietet eine Einführung in Machine Learning und praktische Anwendung mittels Python. Sie werden verschiedene ML-Verfahren wie Neuronale Netze und Random Forests in realen Szenarien mittels des QUA³CK-Prozesses und Frameworks wie Keras und Jupyter Notebook anwenden. | KI Campus / KIT | AMALEA – Angewandte Machine-Learning-Algorithmen | KI-Campus | |
| AutoML - Automated Machine Learning | The "Automated Machine Learning" course delves into designing efficient ML pipelines, focusing on hyperparameters and neural network architectures. It covers topics like Hyperparameter Optimization, Neural Architecture Search, and various AutoML optimization methods. The course is suitable as a MOOC or a blended learning format. | KI Campus | AutoML - Automated Machine Learning | KI-Campus | |
| Machine Learning for all | Machine learning is reshaping various aspects of our lives through its core principle: training algorithms using data. While AI advancements may seem futuristic, they stem from this foundational concept. | University of London | Machine Learning for All | University of London | |
| Building AI | "Building AI" is a flexible online course offering insights into practical AI methods, including machine learning and neural networks. Tailor your learning experience from multiple-choice exercises to Python programming based on your expertise. Complete the course for free, craft an AI idea, and optionally purchase a certificate. | University of Helsinki, Reaktor Education | Course Overview - Building AI (elementsofai.com) | |
| KI und Leadership - Mikrokurs | In diesem kompakten Mikrokurs zu "KI & Leadership" erhalten Sie einen schnellen Einstieg in die Relevanz von Künstlicher Intelligenz für Führungskräfte. Sie lernen zentrale Begriffe wie Algorithmen und Advanced Learning kennen und erkunden den Mehrwert von KI in der Führung und Weiterbildung. Zudem werden ethische Aspekte und Datenschutz im Kontext von KI beleuchtet. | KI-Campus / Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) | https://ki-campus.org/courses/kileadership-dfki2022 | |
| KI und Leadership | Der Kurs „KI und Leadership“ richtet sich an Führungskräfte und Interessierte, um die Vorteile und Implementierung von KI-Technologien im Führungskontext zu verstehen. Er vermittelt Kenntnisse über den Einsatz digitaler Medien und KI-Systeme in der Personalführung, wobei Ethik und Datenschutz im Vordergrund stehen. Die Inhalte reichen von Leadership und KI-Grundlagen über Ethik bis hin zu Führungsstilen und Technologietransfer. | KI-Campus / Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) | https://ki-campus.org/courses/kileadership-dfki2022 | |
| Webinar: Sharing and Deploying Data Science with KNIME Server | The content of the training likely covers the features and functionalities of the KNIME Server, with a focus on how it supports collaborative work and automation of data science workflows. | KNIMETV | https://www.youtube.com/watch?v=2hnIDpI3_C0 | |
| NVIDIA Deep Learning Institute (DLI) | Self-paced training courses covering various topics related to deep learning and accelerated computing. | The NVIDIA Deep Learning Institute (DLI) | https://www.nvidia.com/en-us/training/ | |
| Semantic Web Technologies - openHPI | The course covers topics related to the Semantic Web, including knowledge representation, querying, and applications in the Web of Data. | openHPI | https://open.hpi.de/courses/semanticweb | |
| KI Wissenpool | Welche Funktionen kann KI künftig in Unternehmen übernehmen, um Prozesse zu optimieren und Mitarbeiter:innen zu entlasten? Damit aus einem „use case“ ein „business case“ wird, braucht es das passgenaue Wissen zur KI und zu ihrem Anwendungsfeld. Auf dieser Seite finden Sie konkrete Anwendungsbeispiele von und Wissenswertes zu KI in kleinen und mittleren Unternehmen. | KI Wissens- und Weiterbildungszentrum | https://www.ki-wissens-und-weiterbildungszentrum.de/ki-verstehen-und-nutzen/ | |
| Udemy | Online-Lernplatform mit über 210.000 breit gefächerten Auswahl von Online-Videokursen | Udemy | https://www.udemy.com/ | |
| ChatGPT Prompt Engineering for Developers | The course covers the use of large language models (LLMs), best practices for prompt engineering, and practical applications of LLM APIs for tasks such as summarizing, inferring, and text transformation. | DeepLearning.AI | https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ | |
| DeepLearning.AI Courses | The courses cover a range of AI specializations, including machine learning, deep learning, natural language processing, and AI for medicine. | DeepLearning.AI | https://www.deeplearning.ai/courses/ | |
| DeepLearning.AI Short Courses | The courses focus on enhancing generative AI skills and cover topics such as prompt engineering, building systems with the ChatGPT API, LangChain for LLM application development, and fine-tuning large language models. | DeepLearning.AI. | https://www.deeplearning.ai/short-courses/ | |
| KIDA-Kompetenzgemeinschaft | The webpage titled "KIDA-Kompetenzgemeinschaft" is part of the KIDA initiative. One of the primary objectives of KIDA is to establish a cross-institutional community for knowledge building and exchange related to AI and data. They are currently developing a platform for networking, further education, and knowledge management. Members of the KIDA community gain access to a private section on the website, where they can find video recordings of lectures and soon, information on further training opportunities. The community aims to benefit from a broad spectrum of knowledge and experiences related to AI and data, allowing members to grow together in an inspiring environment. Additionally, there's a newsletter that provides regular updates on events, training, and networking opportunities related to AI and data within the KIDA framework. | KIDA - AI for Food and Agriculture | https://www.kida-bmel.de/services/kompetenzgemeinschaft | |
| Deep Learning Essentials - edX | This course is offered by the Université de Montréal on edX and provides an in-depth overview of deep learning fundamentals. Created in collaboration with Mila and IVADO, the course covers the basics of deep learning, types of neural networks, and hands-on experience with deep learning libraries. Yoshua Bengio, a renowned AI expert and a 2018 A.M. Turing Award recipient, is the scientific director of the course. Designed for professionals with a basic understanding of mathematics and programming, the course spans 5 weeks and requires 4-6 hours of study per week. | Université de Montréal in collaboration with Mila and IVADO | https://www.edx.org/learn/deep-learning/universite-de-montreal-deep-learning-essentials | |
| Databricks Large Language Models Professional Certificate – edX | professional certificate program, offered on edX in collaboration with Databricks, delves into the world of Large Language Models (LLMs). The program aims to equip learners with the skills to build, deploy, and scale LLMs using the Databricks platform. It covers foundational concepts, practical applications, and the ethical considerations of LLMs. The course is designed for data scientists, machine learning engineers, and other professionals looking to harness the power of LLMs in their work. The program's content is structured to provide both theoretical knowledge and hands-on experience. | edX in collaboration with Databricks. | https://www.databricks.com/resources/webinar/build-your-own-large-language-model-dolly?scid=7018Y000001Fi0wQAC&utm_medium=paid+search&utm_source=google&utm_campaign=17147861652&utm_adgroup=150865151987&utm_content=od+webinar&utm_offer=emea-build-your-own-large-language-model-dolly&utm_ad=665972097348&utm_term=databricks%20large%20language%20models&gad_source=1&gclid=CjwKCAiAmsurBhBvEiwA6e-WPEFZMKEvZKfjxj4qAUz4f3obCqjRuYd80BLmihRcWT9bV0-ZdWbFORoC3pYQAvD_BwE | |
| Large Language Models with Semantic Search | The webpage titled "Large Language Models with Semantic Search" is a new course offered by DeepLearning.AI in collaboration with Cohere. The course, designed for beginners, is approximately 1 hour long and is instructed by Jay Alammar and Luis Serrano. The course aims to enhance the traditional keyword search by incorporating large language models (LLMs) to improve the user experience. Participants will learn about dense retrieval, which improves the relevance of search results, and reranking, which enhances search systems using LLM intelligence. By the end of the course, learners will be able to implement LLM-powered search into their projects and understand the basics of keyword search, reranking, and dense retrieval using embeddings. The course is currently free for a limited time during the DeepLearning.AI learning platform beta phase. | DeepLearning.AI in collaboration with Cohere | https://www.deeplearning.ai/short-courses/large-language-models-semantic-search/ | |
| Learning Materials by HIFIS | HIFIs offers a variety of learning materials related to software engineering and the use of cloud services. They also provide an overview of workshops that teach foundational software engineering skills. The workshop materials are available for self-guided learning or can be used as a foundation for personal workshops. These materials are available under a Creative Commons License. Topics covered include research software publication, container virtualization in science, event management with Indico, Python programming, Git and GitLab usage, and more. Additionally, they offer further materials, tutorials, and guidelines on various topics. Those interested in specific workshops for their teams can contact HIFIS directly | HIFIS | https://hifis.net/services/software/training | |
| KNIME L4 Deep Learning Exam | KNIME has introduced the L4-DL exam, which focuses on deep learning. Deep learning, a subset of machine learning, powers many AI applications. Over the years, deep learning architectures have evolved significantly, with advancements like RNNs with LSTM units, CNNs, autoencoders, and GANs. There's a misconception that building deep learning applications requires complex coding. However, tools like the KNIME Analytics Platform offer a no-code/low-code approach to deep learning. KNIME's L4-DL self-paced course teaches codeless deep learning, and the new L4-Deep Learning certification exam allows learners to validate their knowledge. The exam was crafted in collaboration with experts from Reichman University, Ben-Gurion University of the Negev, and KNIME. The certification is suitable for anyone, provided they have passed the L1-L3 certification exams. Upon passing the L4-DL exam, participants receive a badge showcasing their deep learning expertise | KNIME: Open for Innovation | https://www.knime.com/blog/l4-deep-learning-certification-exam | |
| AI Fire | AI Fire offers a comprehensive collection of AI learning resources. This platform provides a wide range of materials, including cheat sheets, AI courses, prompt engineering tutorials, and more | AI Fire | https://www.aifire.co/c/ai-learning-resources | |
| Data Week Leipzig 2024 | Data Utilization, FAIR Principles, Semantics, and AI | Data Week Leipzig | https://2024.dataweek.de/ | |
| Kompakteinstieg: Prompting für Generative KI | Diese Schulung bietet einen praktischen Einstieg in das Thema GPT, Prompting und große Sprachmodelle. Sie erhalten Hintergrundwissen und können Gelerntes mit Hilfe unserer KI-Expert*innen und Data Scientists selbst anwenden. | Fraunhofer-Allianz Big Data und Künstliche Intelligenz | https://www.bigdata-ai.fraunhofer.de/de/data-scientist/schulungssuche/KompakteinstiegPromptingFuerGenerativeKI.html | |