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. *** This is CS50, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming. *** HOW TO SUBSCRIBE HOW TO TAKE CS50 edX: Harvard Extension School: Harvard Summer School: OpenCourseWare: HOW TO JOIN CS50 COMMUNITIES Discord: Ed: Facebook Group: Faceboook Page: GitHub: Gitter: Instagram: LinkedIn Group: LinkedIn Page: Quora: Slack: Snapchat: Twitter: YouTube: HOW TO FOLLOW DAVID J. MALAN Facebook: GitHub: Instagram: LinkedIn: Quora: Twitter: *** CS50 SHOP *** LICENSE CC BY-NC-SA 4.0 Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License David J. Malan malan@