This year the 28th Computational Intelligence workshop  took place on November, 29th-30th, in Dortmund. As every year, this two-day workshop was packed with many interesting talks and two gripping keynotes (deep learning and OpenML). The presentations and the keynotes had diverse topics including fuzzy control, surrogate-assisted optimization, modeling techniques, deep learning, interesting applications (e.g. gait recognition, driving lane recognition) and several other areas

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The deep learning and reinforcement learning summer schools hosted by Canadian Institute For Advanced Research (CIFAR) and the Vector Institute are among the most prestigious summer schools in the field of machine learning and artificial intelligence. This year the summer school was taking place in Toronto, Canada from 25th of July till 3rd of August including 7 days of deep learning school and 3 days of reinforcement learning lectures. Since deep learning became very popular in the last years, a large number of students and researchers (over 1200 applicants) applied to participate in both of these summer schools and a little bit more than 250 delegated to attend. This year two of the PhD students from TH Köln were among the 23% lucky attendees of these valuable summer schools.

Every day of the summer schools was planned with many interesting lectures, contributed talks and a poster session. The lectures covered a wide variety of topics. Also the level of the lectures were varying from basics going to very advanced topics. The lecturers were from DeepMind, Google Brain, Microsoft Research, Vector Institute as well as pioneers of the field from University of Toronto, University of Alberta and the Montreal Institute of Learning Algorithm (MILA) and several other active institutes in the field.  The contributed talks were given by PhD students and were selected by the summer school's organizers committee. Samineh Bagheri and Markus Thill from TH Köln also had a chance to present a poster in this summer school.

We are grateful for partial funding of travel and conference costs by the deanery of the faculty and the computer science institute.

ECDA is an international conference with the main focus on data science and data mining which is being held in different European cities since 2013 almost every year. This year the conference was held in Paderborn, Germany. This 3-days conference hosted many interesting keynotes and a large number of talks given by scientists from all over the world. Prof. Wolfgang Konen organized and chaired the "Time Series Analysis and Online Algorithms"  session. In the same session Markus Thill presented his recent work "Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions" and he received valuable remarks and feedback. The deadline for the paper  submission is on 15th of September and after successfully passing through a reviewing process the work will be published in Archives of Data Science, Series A Journal.

Digital Xchange Conference was held on 23rd of July in TH Köln, Campus Gummersbach as a collaborative work of Opitz consulting company and TH Köln. The event attracted over 600 audience and the talks covered a large area of digitalization field including cloud computing, artifical intelligence, industry 4.0, augemented reality, IT security and many more interesting topics. 

"Machine Learning and AI for Predictive Maintenance" is the title of an interesting presentation given by our research team member Mr. Markus Thill. Markus Thill presented an overview of his PhD research topic which he is pursuing at TH Köln under the supervision of Prof. Wolfgang Konen. He discussed several anomaly detection algorithms and their performances. The talk attracted a lot of attention from the audience.

The R package TDMR (Tuned Data Mining in R) is now available on CRAN in a major new release 2.0. It supports the new R  package SPOT 2.0 (Sequential Parameter Optimization Toolbox) with its largely redesigned and simplified interface. TDMR 2.0 has as well a simplified interface. TDMR documentation and TDMR tutorials have been rewritten to account for the simpler interface.

Tuned Data Mining in R ('TDMR') performs the complete tuning of a data mining task (predictive analytics, that is classification and regression). Preprocessing parameters and modeling parameters can be tuned simultaneously. It incorporates a variety of tuners (among them 'SPOT' and CMA with package 'rCMA') and allows integration of additional tuners. Noise handling in the data mining optimization process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.

More information on TDMR is available on the TDMR project page.