<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Comp9418 on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/comp9418/</link><description>Recent content in Comp9418 on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Mon, 31 Aug 2026 11:43:02 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/comp9418/index.xml" rel="self" type="application/rss+xml"/><item><title>COMP9418 — Advanced Topics in Statistical Machine Learning at UNSW</title><link>https://abaj.ai/blog/pgrad-unsw/comp9418/</link><pubDate>Mon, 31 Aug 2026 08:00:00 +1000</pubDate><guid>https://abaj.ai/blog/pgrad-unsw/comp9418/</guid><description>&lt;p>I took COMP9418 in 2026 T2 as part of my &lt;a
 href="https://abaj.ai/blog/pgrad-unsw/"
 
 
>Masters of Statistics&lt;/a>. Despite the
broad name, the course is really one long story about &lt;em>probabilistic graphical
models&lt;/em>: how to encode a joint distribution as a graph, how to answer queries
against it exactly and approximately, and how to learn it from data. If you
are deciding whether to enrol, or you are mid-term and drowning, this page
collects everything I wish I had on day one.&lt;/p></description></item></channel></rss>