<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Course-Review on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/course-review/</link><description>Recent content in Course-Review 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/course-review/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><item><title>MATH5806 — Applied Regression Analysis at UNSW</title><link>https://abaj.ai/blog/pgrad-unsw/math5806/</link><pubDate>Mon, 31 Aug 2026 08:00:00 +1000</pubDate><guid>https://abaj.ai/blog/pgrad-unsw/math5806/</guid><description>&lt;p>I took MATH5806 in 2026 T2 as part of my &lt;a
 href="https://abaj.ai/blog/pgrad-unsw/"
 
 
>Masters of Statistics&lt;/a>. The name
undersells it: &amp;ldquo;applied regression analysis&amp;rdquo; here means &lt;em>generalised linear
models&lt;/em>, done properly &amp;mdash; exponential families, link functions, iteratively
reweighted least squares, deviance &amp;mdash; with ordinary linear regression as the
special case rather than the destination, and splines, Poisson and binomial
regression along the way. Everything runs in R. This page is the admin,
the textbook, and my notes, in one place.&lt;/p></description></item></channel></rss>