This year beautiful Prague hosted GECCO 2019, from 13th to 17th of July. GECCO -- standing for The Genetic and Evolutionary Computation Conference-- is an outstanding conference which brings many active scientists and PhD students together annually. We have presented a recent piece of our work in form of a poster. The work titled: "Online Whitening for High Conditioning Optimization Problems" is a preliminary work toward addressing the challenges of handling high conditioning functions with surrogates. GECCO provided a great opportunity to recieve interesting feedback and ideas from the optimization community.
173 contribuations were accepted as full papers and 168  contributions as poster by GECCO 2019 committee. High quality paper and poster sessions both enabled valubale chances to exchange opinion and evolve your ideas. Apart from these sessions, GECCO hosted plenty of useful tutorials, workshops, keynotes and social events. 
"Challenges for Learning in Complex Environments" was the name of an outstanding keynote talk given by Raia Hadsell, the Robotics Lead of Google DeepMind. She emphasized on the importance of multiobjective learners and the continual learning concept.
 

We are pleased to inform you that the recent work "General Board Game Playing for Education and Research in Generic AI Game Learning" of Prof. Wolfgang Konen has been accepted for publication and it will be presented as a Poster at IEEE CONFERENCE ON GAMES (CoG) 2019.  CoG 2019, held  in London, UK, will be hosting many peer-reviewed paper and poster presentations as well as academical and industrial invited talks in various game technology related fields.  Wolfgang Konen introduces a general board game playing and learning framework, in his recent paper. You can find more information about the developed educational tool by Wolfgang Konen in one of our former blog-posts.

Recommender System is the name given to any software designed to recommend you objects to purchase, to click on or to watch, which might be attractive to you with a high probability. Development of such systems became a hot topic for software developers and machine learning engineers in the last years. A well-performing recommender system can make significant contributions to various online platforms.

Nowadays, many of us interact with recommender systems on a daily basis. If you have an account in Amazon or Netflix or many similar online services, you may have received suggestions that often fit your interest. This can vary from a movie suggested by Netflix or very various products suggested by Amazon. The targeted advertisements on Facebook or the targeted pop-up advertisements in your browser are also other examples of applications of these systems. Almost no two person receives the exact same advertisements or suggestions on an online platform.  All these personalized suggestions and advertistments use recommender systems to find the best match for your interests according to your former ratings, purchases or clicks.

Content-based recommender systems often make use of the explicit features of the users and the items to provide recommendations. In contrary, the Collaborative Filtering recommender systems do not have access to any sort of explicit features and tend to learn the important features of users and items implicitly. The real-world recommender systems are often a hybrid of both approaches.

You can try our movie recommender system demo, by simply giving your own ratings and see what the system will suggest to you. This movie recommender system is based on the implementation of Matrix Factorization algorithm in Python. Matrix Factorization is a collaborative filtering approach which tends to learn implicit features for users and items by factorizing the exiting rating matrix. We expalin this algorithm in more details, in this Google Colaboratory Notebook . We describe how the recommender system problem turns into an optimization problem and how to handle such optimization tasks. Also a step-by-step implementation of this algorithm in Python is provided.

One of the slides of the presentation given by Henning Buhl during the Digital Xchange 2019 Conference

On Saturday, May, 25th, 2019, TH Köln hosted the annual Digital Xchange conference, which is organized as a cooperative work of Opitz Consulting and TH Köln, for the third time. This 1-day conference is becoming more and more popular: this year it attracted more than 800 participants.  More than 100 talks were given in the field of digitalization and its impact on industry. The topics had a large variety and covered many areas of digitalization including Big Data, Cloud Computing, Industry 4.0, Machine Learning, Artifical Intelligence, etc. You can find a list of all talks here.

One of the very interesting talks at Digital Xchange which attracted a large group of audience was "Deep Learning mit Keras und TensorFlow" given by Henning Buhl. He is a Computer Science Bachelor student at TH Köln who also works part-time under supervision of Prof. Wolfgang Konen. His talk coverd a large range of themes from neural networks to machine learning and Python code examples. As part of his talk he gave a live demo on Google Colaboratory with Jupyter notebooks showing TensorFlow and Keras examples. These examples are available on GitHub for your own study if you like. You can find them here or here on GitHub.

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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