<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sequential Data | Abeer Badawi</title><link>https://abeerbadawi.github.io/tags/sequential-data/</link><atom:link href="https://abeerbadawi.github.io/tags/sequential-data/index.xml" rel="self" type="application/rss+xml"/><description>Sequential Data</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Sep 2025 00:00:00 +0000</lastBuildDate><image><url>https://abeerbadawi.github.io/media/icon_hu_77ffe8aef0ca05e2.png</url><title>Sequential Data</title><link>https://abeerbadawi.github.io/tags/sequential-data/</link></image><item><title>Instructor @ Guelph University</title><link>https://abeerbadawi.github.io/teaching/guelph-instructor/</link><pubDate>Mon, 01 Sep 2025 00:00:00 +0000</pubDate><guid>https://abeerbadawi.github.io/teaching/guelph-instructor/</guid><description>&lt;p&gt;&lt;strong&gt;September 2025 - Current&lt;/strong&gt;&lt;/p&gt;
&lt;h3 id="courses-"&gt;Courses :&lt;/h3&gt;
&lt;p&gt;• &lt;strong&gt;DATA&lt;em&gt;6400&lt;/em&gt;01 Machine Learning for Sequential Data Processing&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Term:&lt;/strong&gt; Winter 2026&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Department:&lt;/strong&gt; Mathematics and Statistics&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Course Description:&lt;/strong&gt; This course emphasizes machine learning for sequential data processing. It covers common challenges and pre-processing techniques such as text, biological sequences, and time series data.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;• &lt;strong&gt;UNIV*6080 Computational Thinking for Artificial Intelligence&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Term:&lt;/strong&gt; Fall 2025&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Department:&lt;/strong&gt; School of Engineering&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Course Description:&lt;/strong&gt; Foundations of Artificial Intelligence and Machine Learning – Covered linear algebra, geometry, matrix decompositions, vector calculus, probability, optimization, and data–model integration; built strong mathematical and computational foundation for AI/ML research.&lt;/li&gt;
&lt;/ul&gt;</description></item></channel></rss>