Oct232018

Cologne AI ML

 

Join us to learn, network, discuss trends and innovations in the AI space, and exchange knowledge with AI experts, machine learning practitioners, and data scientists.

 

The Automatic Data Scientist

Prof. Dr. Kristian Kersting, Head of the Machine Learning Lab, TU Darmstadt, ml-research.github.io:

Our minds make inferences that appear to go far beyond standard data science. Whereas people can learn richer representations and use them for a wider range of learning tasks, machine learning algorithms have been mainly employed in a stand-alone context, constructing a single function from a table of training examples. In this talk, I shall touch upon a view on data science that can help capturing these human learning aspects by combining high-level languages and databases with statistical learning, optimisation, and deep learning. High-level features such as relations, quantifiers, functions, and procedures provide declarative clarity and succinct characterisations of the data science problem at hand. This helps reducing the cost of modelling and solving it. Putting probabilistic deep learning into the data science stack, it even paves the way towards one of my dreams, the automatic data scientist — an AI that makes data analysis and reporting accessible to a broader audience of non-data scientists. This talk is based on joint works with many people such as Carsten Binnig, Martin Grohe, Zoubin Ghahramani, Martin Mladenov, Alejandro Molina, Sriraam Natarajan, Robert Peharz, Cristopher Re, Karl Stelzner, Martin Trapp, Isabel Valera, and Antonio Vergari.

https://twitter.com/TFConsult/status/1054795670908030976

 

Build machine learning models in a few clicks

David Arnu, Senior Data Scientist, RapidMiner, www.linkedin.com/in/david-arnu-680765110:

Building a machine learning model can range from being a standard routine or a very challenging task. But regardless of the task, we always follow the same steps: data import and preparation, model selection, and finally training your model. The new challenge is how to democratize machine learning so that its tools and capabilities can be used by a wider range of users in the workplace, and in particular the new so-called “citizen data scientists”. RapidMiner Studio Auto Model is designed for both new users as well as expert data scientists with a guided, visual workflow that builds machine learning models with no “black box”. In this talk, I’ll demonstrate the capabilities of RapidMiner Auto Model to quickly build, compare, and finally bring machine learning models into production.

 

Networking

CAIML #4 is also supported by AI Spektrum – AI Spektrum provides substantiated and free specialist information. The platform informs about backgrounds, current trends and experiences in the areas of artificial intelligence, machine learning, deep learning, neural networks and robotics.

 

CAIML

Cologne AI and Machine Learning Meetup

www.meetup.com/de-DE/Cologne-AI-and-Machine-Learning-Meetup

 

Credentials

Thanx for an excellent organisation to goedle.io

Categories: Innovation, Open Source, R&D, Software
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Author: Dr. Thomas Fabula

Thomas Fabula applies his profound experience in various functions for multinational corporations and medium-sized enterprises in different branches. As a former athlete his passion for sportive leadership enables him to motivate intercultural & cross-disciplinary teams in business environments to high-performance level.