| Training course (title) | Content | Provider |
|---|---|---|
| Workshop - Ein innovatives Konzept für den digitalen Sammlungsaufbau zur Durchführung von Text-und-Data-Mining-Verfahren (Workshop – An innovative concept for digital collection development for conducting text and data mining processes) [in German] | As part of the project 'Workflow Digital Media' (WDM), funded by the German Research Foundation (DFG), a reusable workflow is being developed that enables libraries to provide scholarly literature in a uniformly structured XML format via freely accessible interfaces. | Universitäts- und Landesbibliothek Darmstadt (ULB) |
| Die Rolle des Data Labelings für Maschinelles Lernen (The Role of Data Labeling for Machine Learning) [in German] | Expert lecture on September 25, 2024: A first insight into the basics and importance of data labeling for high-performance AI models. In addition to AI-based labeling, a use case from agriculture will be presented. | KIDA - AI for Food and Agriculture |
| Elements of AI | The online course Elements of AI offers easy-to-understand AI fundamentals with practical exercises. No prior knowledge required. Developed by Reaktor and the University of Helsinki, the German version is supported by the DIHK-Bildungs-GmbH and the BMWi. | University of Helsinki, MinnaLearn |
| Künstliche Intelligenz und maschinelles Lernen für Einsteiger (Artificial Intelligence and Machine Learning for Beginners) [in German] | The openHPI course for beginners demystifies AI and machine learning. The target audience is non-experts. Course instructors Hötter and Warmuth explain the basics and different learning methods using examples. The course concludes with a look at ethical and technical limitations. Python programming is not part of the course. | OpenHPI |
| KI und Datenqualität - Perspektiven aus Data Science, Ethik, Normung und Recht (AI and data quality - perspectives from data science, ethics, standardization and law) [in German] | Artificial intelligence, especially neural networks, requires high-quality training data. Data quality encompasses informatics, legal, and ethical aspects. Missing or faulty data can lead to unreliable AI models. The course AI and Data Quality addresses these topics in the context of the KITQAR project. | OpenHPI |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Daten- und Algorithmenethik (Data and algorithm ethics) [in German] | Basics of Data and Algorithm Ethics, an overview of culturally influenced moral theories, current AI applications and ethical approaches, as well as integrated ethics games for illustration. | KI-Campus |
| 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 / German Research Center for Artificial Intelligence (DFKI) |
| AutoML - Automated Machine Learning_x000D_ | 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 |
| Künstliche Intelligenz und Maschinelles Lernen in der Praxis (Artificial intelligence and machine learning in practice) [in German] | Following the introductory course on AI and Machine Learning, the follow-up course focuses on the practical application of real data projects under the guidance of Johannes Hötter and Christian Warmuth. All steps of a data-driven project are covered, with four weekly projects such as housing price prediction and sign language recognition. Prior knowledge from the introductory course is required. | OpenHPI |
| 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 |
| AMALEA – Angewandte Machine-Learning-Algorithmen (AMALEA - Applied Machine Learning Algorithms) [in German] | The course provides an introduction to machine learning and practical application using Python. You will apply various ML methods such as neural networks and random forests in real-world scenarios using the QUA³CK process and frameworks such as Keras and Jupyter Notebook. | KI Campus / KIT |
| 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 |
| 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 |
| 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 |
| KI und Leadership - Mikrokurs (AI and leadership - micro course) [in German] | In this compact micro-course on 'AI & Leadership,' you will gain a quick introduction to the relevance of artificial intelligence for leaders. You will learn key terms such as algorithms and advanced learning, and explore the added value of AI in leadership and training. Additionally, ethical aspects and data protection in the context of AI will be addressed. | KI-Campus / German Research Center for Artificial Intelligence (DFKI) |
| KI und Leadership - Kurs (AI and leadership - course | The course 'AI and Leadership' is aimed at executives and interested individuals to understand the benefits and implementation of AI technologies in a leadership context. It provides knowledge about the use of digital media and AI systems in personnel management, with a focus on ethics and data protection. The content ranges from leadership and AI fundamentals to ethics, leadership styles, and technology transfer. | KI-Campus / German Research Center for Artificial Intelligence (DFKI) |
| 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 |
| 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) |
| 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 |
| KI Wissenpool (AI knowledge pool) | On this page you will find specific application examples and interesting facts about AI in small and medium-sized companies. | KI Wissens- und Weiterbildungszentrum |
| Udemy | Online learning platform with over 250,000 wide-ranging selections of online video courses. Topics include Machine Learning, Python, Cyber Security, Ethical Hacking, SQL and others. | Udemy |
| 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 |
| 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 |
| 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 |
| KIDA Competence Community | The KIDA Competence Community 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 |
| 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 |
| 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. |
| 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 |
| 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 |
| 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 |
| Kompakteinstieg: Prompting für Generative KI (Compact entry: Prompting for generative AI) [in German] | This course offers a practical introduction to GPT, prompting and large language models. You will gain background knowledge and can apply what you have learned yourself with the help of our AI experts and data scientists. | Fraunhofer - Big Data AI |