<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0">
    <channel>
        <title>Data-Science - Category - Eric Armbruster</title>
        <link>http://example.org/categories/data-science/</link>
        <description>Data-Science - Category - Eric Armbruster</description>
        <generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>eric.armbruster@tum.de (Eric Armbruster)</managingEditor>
            <webMaster>eric.armbruster@tum.de (Eric Armbruster)</webMaster><lastBuildDate>Fri, 10 Nov 2023 13:55:54 &#43;0100</lastBuildDate><atom:link href="http://example.org/categories/data-science/" rel="self" type="application/rss+xml" /><item>
    <title>Pattern Recognition Resonators</title>
    <link>http://example.org/posts/snn/pattern-recognition-resonators/</link>
    <pubDate>Fri, 10 Nov 2023 13:55:54 &#43;0100</pubDate>
    <author>Eric Armbruster</author>
    <guid>http://example.org/posts/snn/pattern-recognition-resonators/</guid>
    <description><![CDATA[Pattern Recognition Resonators This project demonstrates Resonate-and-Fire (RF) neurons and unsupervised hebbian learning can be combined to build Spiking Neural Networks (SNNs) that are capable of recognizing (complex) patterns in time data.
Results We have no proper evaluation comparing against other state-of-the-art networks yet, as this code was created during a neuromorphic hackathon organized by neurotum x Fortiss. The hyperparameters, input data and output data of the experimental networks are saved in save.]]></description>
</item>
<item>
    <title>ETFOptimizer: A Portfolio Optimization Tool for ETFs</title>
    <link>http://example.org/posts/etfoptimizer/etfoptimizer/</link>
    <pubDate>Sun, 05 Nov 2023 13:38:23 &#43;0100</pubDate>
    <author>Eric Armbruster</author>
    <guid>http://example.org/posts/etfoptimizer/etfoptimizer/</guid>
    <description><![CDATA[Motivation and Goals For the interdisciplinary project (IDP), Ruben and I worked on a portfolio optimization tool for the Chair of Financial Management and Capital Markets at TUM. Our motivation for creating an EtfOptimizer tool was as follows:
 ETFs are a low cost and comparatively low risk investment option with good returns Abundance of ETFs on the market: complicates investment decisions An optimization can:  Take investor preferences into account Asset categories Investment amount Risk tolerance Preferred return    We had the following goals in mind while designing and implementing the etfoptimizer tool:]]></description>
</item>
</channel>
</rss>
