Künstliche Intelligenz Partials
Data Analytics

Time Series Data Clustering Distance Measures

As ubiquitous as time series are, it is often of interest to identify clusters of similar time series in order to gain better insight into the structure of the available data. However, unsupervised learning from time series data has its own stumbling blocks. For this reason, the following article presents some helpful time series specific distance metrics and basic procedures to work successfully with time series data.

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Künstliche Intelligenz Business Prozess
Data Analytics

Personalize Learning to Rank Results through Reinforcement Learning

Learning to optimally rank and personalize search results is a difficult and important topic in scientific information retrieval as well as in online retail business, where we typically want to bias customer query results with respect to specific preferences for the purpose of increasing revenue. Reinforcement learning, as a generic-flexible learning model, is able to bias, e.g. personalize, learning-to-rank results at scale, so that externally specified goals, e.g. an increase in sales and probably revenue, can be achieved. This article introduces the topics learning-to-rank and reinforcement learning in a problem-specific way and is accompanied by the example project ‚cli-ranker‘, a command line tool utilizing reinforcement learning principles for learning user information retrieval preferences regarding text document ranking.

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Künstliche Intelligenz Machine Learning Parts
Data Analytics

Time as a Machine Learning Feature

Quite often it is the case that cyclic data is not sufficiently transformed for machine learning algorithms, e.g. feature representation is missing out on the implicit properties of cyclic features often resulting in wrong distance measures. This article introduces cyclic feature transformation for time based features as a mini-howto.

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Azure Cloud
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Interne JupyterHub Plattform

Auto-Skalierung dank des Azure Kubernetes Cluster, verknüpft mit Azure VM Nodepools und der Nutzung von vorbereiteter Docker-Images über Azure Container Registry. Für die Datenhaltung kommt

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Conrad Electronics SE
Data Analytics
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E-Commerce Visitor Analysis

Besuchsmusteranalyse von Webshop-Besucherdaten mit dem Ziel Handlungsempfehlungen und Entwicklungsmassnahmen zur Churn-Ratenreduzierung und Conversionrate Steigerung abzuleiten.

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Künstliche Intelligenz Partials
Azure Cloud
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Reporting Plattform

Konzeption einer Serverless Architektur in der Microsoft Azure Cloud. Nutzung von Azure API Management, Logic und Function Apps, sowie Azure Storage Account als Data Lake

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