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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.



Industrial Data Science Conference

The Industrial Data Science Conference gathers experts from various industries and focuses on data science applications in industry, use cases, and best practices to foster the exchange of experience, discussions with peers and experts, and learning from presenters and other attendees.

Digitization, the Internet of Things (IoT), the industrial internet, and Industry 4.0 technologies are transforming complete industries and allow the collection of enormous amounts of data of various types, including Big Data and Streaming Data, structured and unstructured data, text, image, audio, and sensor data. Data Science, Data Mining, Process Mining, Machine Learning, and Predictive Analytics offer the opportunity to generate enormous value and a competitive advantage. Typical use cases include demand forecasting, price forecasting, predictive maintenance, machine failure prediction and prevention, critical event prediction and prevention, product quality prediction, process optimization, mixture of ingredients optimization, and assembly plan predictions for new product designs in industries like automotive, aviation, energy, manufacturing, metal, etc.

Join your peers in the analytics community at IDS 2017 as we explore breakthrough research and innovative case studies that discuss how to best create value from your data using advanced analytics.

Date September 5th, 2017
Location TU Dortmund University
Web RapidMiner IDS 2017

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Resource-aware Machine Learning - International Summer School 2017

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Big data in machine learning is the future. But how to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? From September 25th to 28th, TU Dortmund University, Germany, hosts the 4th summer school on resource-aware machine learning. Further information and online registration at: http://sfb876.tu-dortmund.de/SummerSchool2017

Topics of the lectures include: Machine learning on FPGAs, Deep Learning, Probabilistic Graphical Models and Ultra Low Power Learning.

Exercises help bringing the contents of the lectures to life. The PhyNode low power computation platform was developed at the collaborative research center SFB 876. It enables sensing and machine learning for transport and logistic scenarios. These devices provide the background for hands-on experiments with the nodes in the freshly built logistics test lab. Solve prediction tasks under very constrained resources and balance accuracy versus energy.

The summer school is open to advanced graduate, post-graduate students as well as industry professionals from across the globe, who are eager to learn about cutting edge techniques for machine learning with constrained resources.

Excellent students may apply for a student grant supporting travel and accommodation. Deadline for application is July 15th.

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