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.

is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming.

***

HOW TO SUBSCRIBE

TO TAKE CS50

edX: https://cs50.edx.org/
Harvard Extension School: https://cs50.harvard.edu/extension
Harvard Summer School: https://cs50.harvard.edu/summer
OpenCourseWare: https://cs50.harvard.edu/x

HOW TO JOIN CS50 COMMUNITIES

Discord: https://discord.gg/T8QZqRx
Ed: https://cs50.harvard.edu/x/ed
Facebook Group: Page: https://github.com/cs50
Gitter: https://gitter.im/cs50/x
Instagram: Group: Page: https://www.quora.com/topic/CS50
Slack: https://cs50.edx.org/slack
Snapchat: TO FOLLOW DAVID J. MALAN

Facebook: https://github.com/dmalan
Instagram: https://www.quora.com/profile/David-J-Malan
Twitter: SHOP

https://cs50.harvardshop.com/

***

LICENSE

CC BY-NC-SA 4.0
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
https://creativecommons.org/licenses/by-nc-sa/4.0/

David J. Malan
https://cs.harvard.edu/malan