<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cs-6601 on Tee Zhi Yao</title><link>https://teezhiyao.com/tags/cs-6601/</link><description>Recent content in Cs-6601 on Tee Zhi Yao</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Thu, 02 May 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://teezhiyao.com/tags/cs-6601/feed.xml" rel="self" type="application/rss+xml"/><item><title>OMSCS: CS 6601 Artificial Intelligence (Spring 2024)</title><link>https://teezhiyao.com/posts/omscs-cs-6601-artificial-intelligence-spring-2024/</link><pubDate>Thu, 02 May 2024 00:00:00 +0000</pubDate><guid>https://teezhiyao.com/posts/omscs-cs-6601-artificial-intelligence-spring-2024/</guid><description>&lt;p>CS 6601 Artificial Intelligence was my first OMSCS course in Spring 2024. Starting the program with a single course felt like the right call, and AI turned out to be a solid choice — it&amp;rsquo;s a broad survey that covers search, probability, decision theory, and machine learning, all tied together by the thread of building intelligent agents.&lt;/p>
&lt;h2 id="what-the-course-covered">What The Course Covered&lt;/h2>
&lt;p>The course is organised around the idea of an intelligent agent perceiving its environment and acting upon it. It started with search — the foundation of many AI problems. Uninformed search (BFS, DFS, uniform-cost) then informed search with A* and the critical concept of admissible and consistent heuristics. The heuristic quality directly determines whether A* finds the optimal path efficiently or explores the entire space. Local search methods like hill climbing and simulated annealing followed, plus constraint satisfaction problems with backtracking and arc consistency.&lt;/p></description></item></channel></rss>