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00:00:00 - Introduction 00:00:15 - Machine Learning 00:01:15 - Supervised Learning 00:08:11 - Nearest-Neighbor Classification 00:12:30 - Perceptron Learning 00:33:19 - Support Vector Machines 00:39:31 - Regression 00:42:37 - Loss Functions 00:49:33 - Overfitting 00:55:44 - Regularization 00:59:42 - scikit-learn 01:09:57 - Reinforcement Learning 01:13:02 - Markov Decision Processes 01:19:56 - Q-learning 01:38:54 - Unsupervised Learning 01:40:19 - k-means Clustering 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@
