Uncertainty - Lecture 2 - CS50’s Introduction to Artificial Intelligence with Python 2020
00:00:00 - Introduction
00:00:15 - Uncertainty
00:04:52 - Probability
00:09:37 - Conditional Probability
00:17:19 - Random Variables
00:26:28 - Bayes’ Rule
00:34:01 - Joint Probability
00:40:13 - Probability Rules
00:49:42 - Bayesian Networks
01:21:00 - Sampling
01:32:58 - Markov Models
01:44:17 - Hidden Markov Models
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
David J. Malan
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