From 3843c269b74ae7d69b965c7c398500f8b6b9613c Mon Sep 17 00:00:00 2001
From: Aaron Welch <awelch83@users.noreply.github.com>
Date: Fri, 14 Oct 2016 19:36:12 -0500
Subject: [PATCH] Removed CS L333 due to broken link. This fixes issue #213.
 (#214)

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 ###Artificial Intelligence
-- [CS L333](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm) **Introduction to Artificial Intelligence** *IIT Delhi* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
-	- Introduction to Artificial Intelligence- Problem Solving, Game Playing, Knowledge Representation, Handling uncertainty using probabilistic models and Fuzzy Logic. Expert systems and Intelligent agents. Machine Learning, Soft computing and NLP.
-	- [Lectures](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm)
-	- [Assignments](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm)
-	- [Readings](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm#book)
 - [CS 188](http://ai.berkeley.edu/home.html) **Introduction to Artificial Intelligence** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20    " height="20" alt="Lecture Notes" title="Lecture Notes" />
     - This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
     - [Lectures](http://ai.berkeley.edu/lecture_videos.html)