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.
Our cooperation partners
We offer content that was developed with experts from various companies and institutions in the field of Machine Learning. Here some examples of the contributing partners and experts:
Prof. Dr. Patrick van der Smagt
Prof. Dr. Michael Feindt
Professor at Karlsruhe Institute of Technology
We cover the following themes with our portfolio:
Which terms and concepts are important for an understanding of machine learning?
- Differentiation between artificial intelligence, machine learning and deep learning
- Importance of data
- Supervised and unsupervised learning
- Artificial neural networks
- Algorithms and models
- Classification, regression, clustering
- Train, validate and test
- Overfitting and bias
- Ethical AI, explainable AI, fair AI
In which subject areas and for which application examples does machine learning play a central role?
- Image recognition
- Voice recognition
- Sentiment analysis
- Anomaly detection
- Predictive maintenance
- Autonomous driving
- Face recognition
- Chatbots
- Fraud detection
- Virtual personal assistants
- Personalization of offers
Which challenges have to be considered when using machine learning methods?
- Availability of (annotated) data
- Quality of the data, data are representative
- Required computing power and time
- Explainability of the results
- Overfitting and bias
What specific challenges do companies face that want to use machine learning in an industrial context?
- Uncertainty in AI projects: high risk when assessing profitability
- Rapid technological development requires a high degree of flexibility
- Difficult communication between ML and domain experts
- AI products need to be constantly monitored and updated
- Broad understanding of AI required in the organization
Our portfolio is the basis for:
- Programs for specific companies and target groups based on preconfigured courses
- Courses that we configure and combine granularly from existing content according to your needs
- Programs that use a combination with your company's own content
Have a look at some content examples from our offering:
Online Course Machine Learning
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:
Dr. Horatiu Fantana
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.