Online Courses Machine Learning

Machine learning is the central problem-solving method that allows us to take advantage of the massive amounts of data generated in our world today and the availability of this data drives machine learning to new levels. Machine learning is behind applications such as fraud detection, predictive maintenance, personalization of offers, voice and image recognition, autonomous driving, chatbots, face recognition, anomaly detection, and many more.

In our courses, we offer elementary introductions to the methodology, compact presentations “Machine Learning on the High Trail”, in-depth studies on individual topics such as deep Learning or anomaly detection, as well as tutorials with concrete machine learning tasks, using the Jupyter Notebook and introducing the participants to practical applications. We cover a broad spectrum: classification, regression, clustering, supervised learning, unsupervised learning, algorithms such as decision trees, decision forests, neural networks, and convolutional neural networks. We can adapt our tutorials to the environment of the customer.

These courses were developed with experts from Fraunhofer, Prof. van der Smagt (Datalab Munich), ZEISS, and more.

Prof. Dr. Patrick van der Smagt

Director of AI Research, Volkswagen Group

  • Machine Learning on the High Trail

    • EN
    • 2 Chapters
    • approx. 2h

    This is a concise introduction that does not require any prior knowledge. We explain why machine learning is a branch of probability theory, introduce some machine learning tasks and algorithms, and to an understanding of neural networks. Machine Learning on the High Trail was produced in a collaboration with Prof. van der Smagt, a leading researcher in machine learning in Europe.

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  • Deep Learning Tutorial

    • EN
    • 5 Chapters
    • approx. 3h

    Deep Learning Tutorial explains how to train convolutional neural networks, a class of neural networks that is especially suited for image processing. It explains important methods like gradient descent and some mathematical principles. You will learn about the role and function of important hyperparameters and how to optimize them. Finally, you can use the Tensor Flow Playground and test the behavior of neural networks under the influence of important hyperparameters. This tutorial was created with experts from ZEISS.

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Dr. Wolfgang Huhn

Wolfgang supports customers in the development of tailored curricula and learning paths based on the requirements of customer-specific digital transformations. Wolfgang was a Senior Partner at the McKinsey & Company office in Germany, where he worked primarily in the semiconductor, optics, energy equipment, and industrial IT industries. He studied physics and electrical engineering at RWTH Aachen University, where he earned his doctorate in theoretical physics.

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