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Stefan Michaelis
63 followers -
Try - Fail - Evolve - Repeat
Try - Fail - Evolve - Repeat

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Resource-aware Machine Learning – International Summer School 2017

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.

http://sfb876.tu-dortmund.de/SummerSchool2017

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Research assistant (m/f) on machine learning at TU Dortmund University, Germany

Application deadline 8th of December, details see: http://sfb876.tu-dortmund.de/DOCUMENTE/ResearchAssistant_w39-16.pdf

The Faculty for Computer Science at TU Dortmund
University, Germany, is looking for a Research Assistant (m/f) with a strong background in Machine Learning/Data Mining, to start at the next possible date and for the duration of up to three years.

The position provides the opportunity to obtain a Ph.D.

The Department of Artificial Intelligence at Dortmund is a small team that is involved in international research on Machine Learning and Data Mining, and develops application-oriented theories as well as theoretically well-founded applications.

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Unser Sonderforschungsbereich auf dem Westfalenkongress zum Thema Big Data.

Video (German only) of the Westfalenkongress on Big Data and the SFB 876 research center participating.

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Resource-aware Machine Learning – International Summer School 2014

x-post from the Machine Learning community, as this should be relevant for the Data community as well

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 29th of September to 2nd of October, the TU Dortmund University, Germany, will host this summer school on resource-aware machine learning. Further information and online registration at: http://sfb876.tu-dortmund.de/SummerSchool2014

Topics of the lectures include: Data stream analysis. Energy efficiency for multi-core embedded processors. Factorising huge matrices for clustering. Using smartphones to detect astro particles.

Exercises help bringing the contents of the lecture to life. All participants get the chance to learn how to transform a smartphone into an extra-terrestial particle detector using machine learning.

The summer school is open for international PhD, advanced master students and practitioners, who want to learn 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 30th of June. 

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Resource-aware Machine Learning – International Summer School 2014

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 29th of September to 2nd of October, the TU Dortmund University, Germany, will host this summer school on resource-aware machine learning. Further information and online registration at: http://sfb876.tu-dortmund.de/SummerSchool2014

Topics of the lectures include: Data stream analysis. Energy efficiency for multi-core embedded processors. Factorising huge matrices for clustering. Using smartphones to detect astro particles.

Exercises help bringing the contents of the lecture to life. All participants get the chance to learn how to transform a smartphone into an extra-terrestial particle detector using machine learning.

The summer school is open for international PhD or advanced master students, who want to learn 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 30th of June. 

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Polar timer for efficient time tracking and management

Even your dream job will include phases full of boring routine work and the risk of meeting our good old friend, procrastination. Several time management techniques try to increase efficiency by focusing on the work in small time slots. To track the number of slots and elapsed time per slot, I decided to build my own online tracker, the polar timer: http://timer.stefan-michaelis.name

The timer comes preconfigured for the popular Pomodoro technique (http://www.pomodorotechnique.com/). This getting-things-done concept splits tasks into 25 minute time slots for focused work, followed by a short 5 minute period to replenish your mental energy and a longer pause every four work slots.

The polar timer consists of three independent circular timers, which can be customized for the runtime in minutes. Optionally, a sound or desktop notification on timer completion, depending on support by your browser, can be activated.

#pomodoro #timemanagement
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And the Euro 2012 data mining winner is...

Soccer games take 90 minutes. And it seems predicting the winner is still hard. Read the wrap-up about using data mining techniques for predicting Euro 2012 match outcomes and which method won the most money:
http://sfb876.tu-dortmund.de/Blog/EM2012/index.html#dj6laasoao

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We know who will win the European Soccer Championship...

Predicting the outcome of soccer matches is hard? A team of graduates of the collaborative research center SFB 876 tries to prove the opposite. Follow them on their  Data Mining journey, not only predicting the winning teams, but demonstrating the whole process from data retrieval up to model training.

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Mining your smartphone made easy

Modern smartphones provide nearly unlimited details about the owner’s life. Carefully mining this data enables detecting valuable knowledge for optimization of the phone’s energy consumption or network resources. A data mining competition is organized as part of the Summer School on Resource-Aware Machine Learning, unlocking some of these capabilities and giving you the chance to explore real life mobile phone data:
http://sfb876.tu-dortmund.de/SummerSchool2012/competition.html

For demonstration purposes, a python script that allows you to create your own data set on any Android smartphone is available for download on the summer school’s homepage.

The summer school will be held from 4th to 7th of September in Dortmund, Germany: http://sfb876.tu-dortmund.de/SummerSchool2012/index.html

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Resource-aware Machine Learning – International Summer School 2012

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 4th to 7th of September, the TU Dortmund University, Germany, will host this summer school on resource-aware machine learning. Further information and online registration at: http://sfb876.tu-dortmund.de/SummerSchool2012

Topics of the lectures include: Mining of ubiquitous data streams, criteria for efficient model selection or dealing with energy constraints...
The theoretical lessons are accompanied by exercises and practical introductions: Analysis with RapidMiner and R, massively parallel programming with CUDA.
A Data Mining Competition lets you test your machine learning skills on real world smartphone data.

The summer school is open for for international PhD or advanced master students, who want to learn 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 1st of June.

If you like the topics we cover with the summer school, please share it with colleagues and friends or give it a + 1.
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