<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Teaching | Sara Candussio</title><link>https://gaoithee.github.io/saracandussio.github.io/teaching/</link><atom:link href="https://gaoithee.github.io/saracandussio.github.io/teaching/index.xml" rel="self" type="application/rss+xml"/><description>Teaching</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sat, 01 Mar 2025 00:00:00 +0000</lastBuildDate><image><url>https://gaoithee.github.io/saracandussio.github.io/media/icon_hu7729264130191091259.png</url><title>Teaching</title><link>https://gaoithee.github.io/saracandussio.github.io/teaching/</link></image><item><title>Probabilistic Machine Learning</title><link>https://gaoithee.github.io/saracandussio.github.io/teaching/probabilistic-ml/</link><pubDate>Sat, 01 Mar 2025 00:00:00 +0000</pubDate><guid>https://gaoithee.github.io/saracandussio.github.io/teaching/probabilistic-ml/</guid><description>&lt;p>Teaching assistant for the &lt;strong>Probabilistic Machine Learning&lt;/strong> MSc course at the University of Trieste (Spring 2025).&lt;/p>
&lt;p>Topics covered: ERM, PAC learnability, Probabilistic Graphical Models, Hidden Markov Models, Bayesian Classification and Regression, Sampling‑based Inference, Expectation‑Maximization, Variational Inference, Generative Modeling (VAEs, Diffusion Models), Gaussian Processes.&lt;/p></description></item></channel></rss>