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	<title>optimization</title>
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	<description>FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</description>
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	<title>optimization</title>
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	<item>
		<title>EECI-IGSC 2025 (M18): Control and Machine Learning</title>
		<link>https://dcn.nat.fau.eu/events/eeci-igsc-2025/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 22 Oct 2024 11:19:27 +0000</pubDate>
				<category><![CDATA[Control]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[EECIIGSC2022]]></category>
		<category><![CDATA[M20]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[ML]]></category>
		<category><![CDATA[optimization]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?post_type=mec-events&#038;p=29996</guid>

					<description><![CDATA[<img width="1200" height="627" src="https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2025_EZuazua_MLazar_junJul2025_feb2025.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2025_EZuazua_MLazar_junJul2025_feb2025.png 1200w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2025_EZuazua_MLazar_junJul2025_feb2025-300x157.png 300w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2025_EZuazua_MLazar_junJul2025_feb2025-1024x535.png 1024w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2025_EZuazua_MLazar_junJul2025_feb2025-768x401.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> Date: June 30 to July 4, 2025 Event: IEECI-IGSC 2025: International Graduate School on Control 2025 Module Control and Machine Learning (M18 DUBROVNIK) Speakers: • Prof. Enrique Zuazua. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg • Prof. Martin Lazar. University of Dubrovnik Outline • Historical preliminaries • Control of linear finite-dimensional systems • The universal approximation theorem • Control [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>TRR 154 Summer School on Optimization, Uncertainty and AI</title>
		<link>https://dcn.nat.fau.eu/trr-154-summer-school-on-optimization-uncertainty-and-ai/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 17 Jan 2024 12:30:56 +0000</pubDate>
				<category><![CDATA[News]]></category>
		<category><![CDATA[Talk]]></category>
		<category><![CDATA[Workshop]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[TRR154]]></category>
		<category><![CDATA[Turnipike]]></category>
		<category><![CDATA[Uncertainty]]></category>
		<category><![CDATA[workshop]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=27994</guid>

					<description><![CDATA[Next Summer, on Wednesday August 7, 2024 our Akad. Director Prof. Dr. Martin Gugat will talk on &#8220;The turnpike property and stabilization&#8221; at the TRR 154 Summer School on Optimization, Uncertainty and AI organized on August 7-9, 2024 at Universität Hamburg. Abstract. TBA WHEN Wed. August 7, 2024 at 14:00H (local time) WHERE Gästehaus der [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Tutorial lecture series on &#8220;Advanced Topics Systems Control&#8221;</title>
		<link>https://dcn.nat.fau.eu/events/tutorial-lecture-series-on-advanced-topics-systems-control/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 21 Nov 2023 04:09:54 +0000</pubDate>
				<category><![CDATA[Control]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[EECIIGSC2022]]></category>
		<category><![CDATA[M20]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[ML]]></category>
		<category><![CDATA[optimization]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?post_type=mec-events&#038;p=27705</guid>

					<description><![CDATA[<img width="1200" height="627" src="https://dcn.nat.fau.eu/wp-content/uploads/tutorialSysCntrl_EZuazua_nov2023.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" srcset="https://dcn.nat.fau.eu/wp-content/uploads/tutorialSysCntrl_EZuazua_nov2023.png 1200w, https://dcn.nat.fau.eu/wp-content/uploads/tutorialSysCntrl_EZuazua_nov2023-300x157.png 300w, https://dcn.nat.fau.eu/wp-content/uploads/tutorialSysCntrl_EZuazua_nov2023-1024x535.png 1024w, https://dcn.nat.fau.eu/wp-content/uploads/tutorialSysCntrl_EZuazua_nov2023-768x401.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> On November 23 &#8211; 27, our Head Prof. Enrique Zuazua is giving a historical introduction to Control and Machine Learning and then talking on control of finite dimensional systems and Supervised Learning by Control Methods at the Tutorial lecture series on &#8220;Advanced Topics Systems Control&#8221; organized (online) from November 23 to December 29 with the [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>EECI-IGSC 2024: Control and Machine Learning</title>
		<link>https://dcn.nat.fau.eu/events/eeci-igsc-2024/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 04 Oct 2023 08:36:54 +0000</pubDate>
				<category><![CDATA[Control]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[EECIIGSC2022]]></category>
		<category><![CDATA[M20]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[ML]]></category>
		<category><![CDATA[optimization]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?post_type=mec-events&#038;p=27415</guid>

					<description><![CDATA[<img width="1200" height="627" src="https://dcn.nat.fau.eu/wp-content/uploads/eeciIgsc2024_eZuazua_mLazar.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" srcset="https://dcn.nat.fau.eu/wp-content/uploads/eeciIgsc2024_eZuazua_mLazar.png 1200w, https://dcn.nat.fau.eu/wp-content/uploads/eeciIgsc2024_eZuazua_mLazar-300x157.png 300w, https://dcn.nat.fau.eu/wp-content/uploads/eeciIgsc2024_eZuazua_mLazar-1024x535.png 1024w, https://dcn.nat.fau.eu/wp-content/uploads/eeciIgsc2024_eZuazua_mLazar-768x401.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> Next year, Prof. Enrique Zuazua (FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg) joint with Prof. Martin Lazar (University of Dubrovnik) will talk on &#8220;Control and Machine Learning&#8221; (M18 DUBROVNIK module) on July 1-5, 2024 at the IEECI-IGSC 2024: International Graduate School on Control 2024 organized by EECI – European Embedded Control Institute and IEEE CSS and IFAC &#8211; International [&#8230;]]]></description>
		
		
		
			</item>
		<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>
		<item>
		<title>Practical course: Modeling, simulation, optimization</title>
		<link>https://dcn.nat.fau.eu/practical-course-modeling-simulation-optimization/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 13 Jun 2022 19:44:45 +0000</pubDate>
				<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Daniël Veldman]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[mathematics]]></category>
		<category><![CDATA[Maths]]></category>
		<category><![CDATA[MATLAB]]></category>
		<category><![CDATA[Modeling]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[simulation]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=19760</guid>

					<description><![CDATA[Practical course: Modeling, simulation, optimization FAU DCN-AvH. Friedrich-Alexander Universität Erlangen-Nürnberg (Germany) Period: Summer semester 2021 This course provides a practical introduction to some of the most commonly used discretization methods for PDEs (finite differences and finite elements) and their implementation in MATLAB. It also covers some of the basics of gradient-based optimization focused on the [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>EECI-IGSC 2022: Machine Learning, Optimization and Control by Prof. E. Zuazua and Prof. X. Yuan</title>
		<link>https://dcn.nat.fau.eu/events/eeci-igsc-2022-machine-learning-optimization-and-control-by-prof-e-zuazua-and-prof-x-yuan/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 20 Oct 2021 06:47:54 +0000</pubDate>
				<category><![CDATA[Control]]></category>
		<category><![CDATA[course]]></category>
		<category><![CDATA[EECIIGSC2022]]></category>
		<category><![CDATA[M20]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[ML]]></category>
		<category><![CDATA[optimization]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/events/eeci-igsc-2022-machine-learning-optimization-and-control-by-prof-e-zuazua-and-prof-x-yuan/</guid>

					<description><![CDATA[<img width="1600" height="900" src="https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2022-EZuazua-XYuan-M20.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" loading="lazy" srcset="https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2022-EZuazua-XYuan-M20.png 1600w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2022-EZuazua-XYuan-M20-300x169.png 300w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2022-EZuazua-XYuan-M20-1024x576.png 1024w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2022-EZuazua-XYuan-M20-768x432.png 768w, https://dcn.nat.fau.eu/wp-content/uploads/EECIIGSC2022-EZuazua-XYuan-M20-1536x864.png 1536w" sizes="auto, (max-width: 1600px) 100vw, 1600px" /> This year, our Head Prof. Enrique Zuazua (Friedrich-Alexander-Universität, Erlangen-Nürnberg) joint with Prof. Xiaoming Yuan (University of Hong Kong) will be talking (M20 – Hong Kong module) about “Machine Learning, Optimization and Control” on June 27th. – July 1st., 2022 at the IEECI-IGSC 2022: International Graduate School on Control 2022 organized by EECI – European Embedded [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Deep Learning and Paradigms</title>
		<link>https://dcn.nat.fau.eu/deep-learning-and-paradigms/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 01 Mar 2021 13:53:16 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Sergi Andreu]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[Deep Neural Networks]]></category>
		<category><![CDATA[DL]]></category>
		<category><![CDATA[generalization]]></category>
		<category><![CDATA[Gradient descent]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[Residual Neural Network]]></category>
		<category><![CDATA[ResNets]]></category>
		<category><![CDATA[stochastic properties]]></category>
		<category><![CDATA[target function]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=3561</guid>

					<description><![CDATA[Deep Learning and Paradigms By Sergi Andreu // This post is the 2nd. part of the &#8220;Opening the black box of Deep Learning&#8221; post Deep Learning Now that we have some intuition about the data, it&#8217;s time to focus on how to approximate the functions that would fit that data. When doing *supervised learning*, we [&#8230;]]]></description>
		
		
		
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