<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>neural networks</title>
	<atom:link href="https://dcn.nat.fau.eu/tag/neural-networks/feed/" rel="self" type="application/rss+xml" />
	<link>https://dcn.nat.fau.eu</link>
	<description>FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</description>
	<lastBuildDate>Thu, 02 Nov 2023 14:25:08 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.0</generator>

<image>
	<url>https://dcn.nat.fau.eu/wp-content/uploads/cropped-FAUDCNAvHlogo_square_silhouette_512x512-32x32.png</url>
	<title>neural networks</title>
	<link>https://dcn.nat.fau.eu</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Math Crash Course</title>
		<link>https://dcn.nat.fau.eu/math-crash-course/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Jun 2023 07:58:38 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Michael Schuster]]></category>
		<category><![CDATA[Course]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Control]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[mathematics]]></category>
		<category><![CDATA[Maths]]></category>
		<category><![CDATA[MATLAB]]></category>
		<category><![CDATA[Modeling]]></category>
		<category><![CDATA[neural networks]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[simulation]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=26947</guid>

					<description><![CDATA[Math Crash Course FAU DCN-AvH. Friedrich-Alexander Universität Erlangen-Nürnberg (Germany) Period: 2023 _ The math final exams are coming up and you&#8217;re still unsure? Don&#8217;t worry, with our Math Abi Crash Course, we can support you in mastering the Math Abi! In this course, we will work together on past year&#8217;s exam tasks with a focus [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Course: A Practical Introduction to Control, Numerics, and Machine Learning (IFAC CPDE 2022)</title>
		<link>https://dcn.nat.fau.eu/course-a-practical-introduction-to-control-numerics-and-machine-learning-ifac-cpde-2022/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 07 Sep 2022 14:48:17 +0000</pubDate>
				<category><![CDATA[2022-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Daniël Veldman]]></category>
		<category><![CDATA[Akademy Enrique Zuazua]]></category>
		<category><![CDATA[Course]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Control]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[mathematics]]></category>
		<category><![CDATA[Maths]]></category>
		<category><![CDATA[MATLAB]]></category>
		<category><![CDATA[Modeling]]></category>
		<category><![CDATA[neural networks]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[simulation]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=22073</guid>

					<description><![CDATA[Practical course: Modeling, simulation, optimization FAU DCN-AvH. Friedrich-Alexander Universität Erlangen-Nürnberg (Germany) Period: Summer 2022 (IFAC CPDE 2022 Course) _ This course gives a general introduction and some recent developments on the interface between Control, Numerics, and Machine Learning (Supervised Learning and Universal Approximation). The first part of the course is a general introduction to important [&#8230;]]]></description>
		
		
		
			</item>
	</channel>
</rss>
