<?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>machine learning &#8211; Penn AI Tech</title>
	<atom:link href="https://resources.pennaitech.org/tag/machine-learning/feed/" rel="self" type="application/rss+xml" />
	<link>https://resources.pennaitech.org</link>
	<description>Just another Penn Nursing site</description>
	<lastBuildDate>Thu, 11 Dec 2025 20:55:46 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.1.2</generator>

<image>
	<url>https://resources.pennaitech.org/wp-content/uploads/sites/39/2023/03/pennaitech-sitelogo-small-32x32.png</url>
	<title>machine learning &#8211; Penn AI Tech</title>
	<link>https://resources.pennaitech.org</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>AI Campus Program</title>
		<link>https://resources.pennaitech.org/ai-campus-program/</link>
		
		<dc:creator><![CDATA[Elizabeth]]></dc:creator>
		<pubDate>Thu, 11 Dec 2025 20:45:40 +0000</pubDate>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Training]]></category>
		<guid isPermaLink="false">https://resources.pennaitech.org/?p=653</guid>

					<description><![CDATA[AI Campus is a collaborative, project-based-learning initiative designed to equip participants with the confidence and skills needed to apply artificial intelligence (AI) methods in their career or research. It brings together participants of diverse backgrounds with top AI experts from &#8230; <a class="kt-excerpt-readmore more-link" href="https://resources.pennaitech.org/ai-campus-program/">Read More</a>]]></description>
										<content:encoded><![CDATA[<p>AI Campus is a collaborative, project-based-learning initiative designed to equip participants with the confidence and skills needed to apply artificial intelligence (AI) methods in their career or research. It brings together participants of diverse backgrounds with top AI experts from around the country to address challenging scientific problems using AI and machine learning (ML). This site provides information on the National AI Campus Program as well as the ‘medicine-focused’ AI Campus Program at the Cedars Sinai Medical Center. Included are training project resources focuses on students getting experience working with AI/ML tools on biomedical problems.</p>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>HEROS (Heuristic Evolutionary Rule Optimization System)</title>
		<link>https://resources.pennaitech.org/heros/</link>
		
		<dc:creator><![CDATA[Elizabeth]]></dc:creator>
		<pubDate>Thu, 11 Dec 2025 20:55:46 +0000</pubDate>
				<category><![CDATA[Machine Learning Modeling]]></category>
		<category><![CDATA[Technology: Tools, Hardware, and Software]]></category>
		<category><![CDATA[modeling]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://resources.pennaitech.org/?p=655</guid>

					<description><![CDATA[HEROS (Heuristic Evolutionary Rule Optimization System) is an evolutionary rule-based machine learning (ERBML) algorithm framework for supervised learning. This scikit-learn compatible machine learning modeling package is designed to agnostically model simple/complex and/or clean/noisy problems (without hyperparameter optimization) and yield maximally &#8230; <a class="kt-excerpt-readmore more-link" href="https://resources.pennaitech.org/heros/">Read More</a>]]></description>
										<content:encoded><![CDATA[<p>HEROS (Heuristic Evolutionary Rule Optimization System) is an evolutionary rule-based machine learning (ERBML) algorithm framework for supervised learning. This scikit-learn compatible machine learning modeling package is designed to agnostically model simple/complex and/or clean/noisy problems (without hyperparameter optimization) and yield maximally human interpretable models. HEROS adopts a two-phase approach separating rule optimization, and rule-set (i.e. model) optimization, each with distinct multi-objective Pareto-front-based optimization. Rules are optimized based on maximizing rule-accuracy and instance coverage using a Pareto-inspired rule fitness function. Differently, models are optimized based on maximizing balanced accuracy and minimizing rule-set size using an NSGA-II-inspired evolutionary algorithm.</p>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>Rule-Based Machine Learning</title>
		<link>https://resources.pennaitech.org/rule-based-machine-learning/</link>
		
		<dc:creator><![CDATA[Ray]]></dc:creator>
		<pubDate>Sun, 05 Mar 2023 21:03:44 +0000</pubDate>
				<category><![CDATA[Internal]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Training Resources]]></category>
		<category><![CDATA[All Resources]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[epistasis]]></category>
		<category><![CDATA[interactions]]></category>
		<category><![CDATA[genetic heterogeneity]]></category>
		<category><![CDATA[interpretable]]></category>
		<category><![CDATA[video]]></category>
		<category><![CDATA[youtube]]></category>
		<guid isPermaLink="false">https://resources.pennaitech.org/?p=228</guid>

					<description><![CDATA[A collection of educational videos focusing on rule-based machine learning and/or the more specific family of &#8216;learning classifier system&#8217; machine learning algorithms. These algorithms are uniquely able to detect, model, and characterize complex multivariate associations in data while yielding much &#8230; <a class="kt-excerpt-readmore more-link" href="https://resources.pennaitech.org/rule-based-machine-learning/">Read More</a>]]></description>
										<content:encoded><![CDATA[<p><span style="font-weight: 400">A collection of educational videos focusing on rule-based machine learning and/or the more specific family of &#8216;learning classifier system&#8217; machine learning algorithms. These algorithms are uniquely able to detect, model, and characterize complex multivariate associations in data while yielding much more interpretable models.</span></p>
]]></content:encoded>
					
		
		
			</item>
	</channel>
</rss>

<!--
Performance optimized by W3 Total Cache. Learn more: https://www.boldgrid.com/w3-total-cache/?utm_source=w3tc&utm_medium=footer_comment&utm_campaign=free_plugin

Page Caching using Disk: Enhanced 
Database Caching 107/137 queries in 2.958 seconds using Disk (Request-wide modification query)

Served from: resources.pennaitech.org @ 2026-10-05 02:08:19 by W3 Total Cache
-->