Optimization - Lecture 3 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Optimization
00:01:20 - Local Search
00:07:24 - Hill Climbing
00:29:43 - Simulated Annealing
00:40:43 - Linear Programming
00:51:03 - Constraint Satisfaction
00:59:17 - Node Consistency
01:03:03 - Arc Consistency
01:16:53 - Backtracking Search

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

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LICENSE

CC BY-NC-SA 4.0
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
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David J. Malan
https://cs.harvard.edu/malan
[email protected]