<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Teaching | Vahan Arsenyan</title><link>https://vahanarsenian.github.io/teaching/</link><atom:link href="https://vahanarsenian.github.io/teaching/index.xml" rel="self" type="application/rss+xml"/><description>Teaching</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 01 Sep 2024 00:00:00 +0000</lastBuildDate><image><url>https://vahanarsenian.github.io/media/icon_hu_1db1b1432e4367d4.png</url><title>Teaching</title><link>https://vahanarsenian.github.io/teaching/</link></image><item><title>Simulation and Monte Carlo Methods (TA)</title><link>https://vahanarsenian.github.io/teaching/ensae-monte-carlo/</link><pubDate>Sun, 01 Sep 2024 00:00:00 +0000</pubDate><guid>https://vahanarsenian.github.io/teaching/ensae-monte-carlo/</guid><description>&lt;p&gt;Teaching assistant for &lt;strong&gt;Simulation and Monte Carlo Methods&lt;/strong&gt;, taught by
&lt;strong&gt;Prof. Nicolas Chopin&lt;/strong&gt; at &lt;strong&gt;ENSAE, Institut Polytechnique de Paris&lt;/strong&gt;
(Sep 2024 – 2026). The course covers:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Random variable generation and Monte Carlo simulation&lt;/li&gt;
&lt;li&gt;Estimation error control and variance reduction&lt;/li&gt;
&lt;li&gt;Importance sampling, stratification, and Latin hypercube sampling&lt;/li&gt;
&lt;li&gt;Quasi-Monte Carlo, low-discrepancy sequences, and numerical integration&lt;/li&gt;
&lt;/ul&gt;</description></item><item><title>Machine Learning II</title><link>https://vahanarsenian.github.io/teaching/ml-ii-ysu/</link><pubDate>Wed, 01 Feb 2023 00:00:00 +0000</pubDate><guid>https://vahanarsenian.github.io/teaching/ml-ii-ysu/</guid><description>&lt;p&gt;Graduate-level &lt;strong&gt;Machine Learning II&lt;/strong&gt; at &lt;strong&gt;Yerevan State University&lt;/strong&gt;,
delivered over two consecutive spring semesters. The course builds on a
first ML course and covers:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Probabilistic modeling and Bayesian inference&lt;/li&gt;
&lt;li&gt;Gaussian processes and Bayesian optimization&lt;/li&gt;
&lt;li&gt;Modern deep learning (architectures, optimization, generalization)&lt;/li&gt;
&lt;li&gt;Causal inference foundations&lt;/li&gt;
&lt;li&gt;Research reading and project work&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Alongside the lectures I supervised student research groups on applied
probabilistic modeling and related topics.&lt;/p&gt;</description></item><item><title>Machine Learning Curriculum (ACA)</title><link>https://vahanarsenian.github.io/teaching/aca-ml/</link><pubDate>Wed, 01 Jun 2022 00:00:00 +0000</pubDate><guid>https://vahanarsenian.github.io/teaching/aca-ml/</guid><description>&lt;p&gt;Designed and delivered the &lt;strong&gt;Machine Learning&lt;/strong&gt; curriculum at the
&lt;strong&gt;Armenian Code Academy (ACA)&lt;/strong&gt; across two summer cohorts. Target
audience: industry-bound practitioners with a software background.
Topics:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Foundations of classical ML (linear models, trees, ensembles)&lt;/li&gt;
&lt;li&gt;Feature engineering, model evaluation, and experiment design&lt;/li&gt;
&lt;li&gt;Deep learning essentials (CNNs, RNNs, Transformers)&lt;/li&gt;
&lt;li&gt;Capstone project track where students shipped end-to-end models&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;The program emphasizes reproducible, production-ready ML practice.&lt;/p&gt;</description></item></channel></rss>