Machine learning is the central problem-solving method and technology that makes it possible to benefit from the gigantic amounts of data generated by sensors, “things” and people. The ever faster growing availability of data and computing power drives machine learning to ever new successes. Machine learning stands behind technology fields such as speech and image recognition, anomaly recognition and predictive maintenance. Many applications such as autonomous driving, face recognition, fraud detection, chatbots or the personalization of offers are also based on this innovative technology, which is part of Artificial Intelligence (AI).
Our portfolio of online courses and online learning content on Machine Learning covers a wide range of topics. We explain the basics and important concepts of machine learning, discuss application examples, and also dive deeper into individual topics such as deep learning or anomaly detection. Training courses and tutorials with specific machine learning tasks use Jupyter notebooks to introduce people to practice. The content covers a broad spectrum: classification, regression, clustering, supervised learning, unsupervised learning, algorithms such as decision tree, random forest and, in particular, artificial neural networks and convolutional neural networks (CNN). Our portfolio of online learning content is the starting point for creating tailor-made training courses for your company and your employees. The content in this area was created with a number of companies and institutions, including experts from Fraunhofer, Professor van der Smagt (Datalab Munich), ZEISS, and Blue Yonder.
Machine Learning on the High Trail | ENG | 6 chapters | approx. 5 h
This course takes you on a journey from what machine learning means and why it is a branch of probability theory, to the main tasks and algorithms of machine learning, to understanding neural networks. No prior knowledge is required for this introduction. Machine Learning on the High Trail was produced in collaboration with Prof. van der Smagt, a leading researcher in the field of machine learning in Europe.
Deep Learning Tutorial | ENG | 5 chapters | approx. 3 h
The Deep Learning Tutorial explains how to train convolutional neural networks, a class of neural networks particularly well suited for image processing. You will learn about important methods like gradient descent, mathematical principles, and the role and function of hyperparameters and how to optimize them. Finally, you can develop some intuition about the behaviour of neural networks by playing with a neural network and tweaking hyperparameters in the Tensorflow Playground.
The Deep Learning Tutorial was created with experts from ZEISS.
Your contact person for Machine Learning at University4Industry:
Horatiu, who holds a PhD in biophysics, heads the Machine Learning and Artificial Intelligence department. He is concerned with how complex topics relating to the analysis and use of data can be conveyed in a practical and easy-to-understand way. Together with customers and partners, he is constantly developing new learning content and formats. The focus is always on the transfer to practical application in industry and companies.
Almost every day, well-known companies find themselves in the headlines because they have been the victim of a cyberattack. For a long time now, security training and the development of measures have no longer been about the question of whether one will be affected, but when and to what extent. However, this does not mean that manufacturing companies should just sit and wait until the time comes. In this paper, we have listed what companies can do in advance to delay attacks and be as resilient as possible to attacks.
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