00:00:00 - Introduction
00:00:15 - Language
00:04:55 - Syntax and Semantics
00:10:23 - Context-Free Grammar
00:20:35 - nltk
00:28:00 - n-grams
00:30:28 - Tokenization
00:38:00 - Markov Models
00:42:41 - Bag-of-Words Model
00:46:38 - Naive Bayes
01:09:18 - Information Retrieval
01:12:06 - tf-idf
01:21:04 - Information Extraction
01:30:13 - WordNet
01:32:06 - Word Representation
01:38:18 - word2vec

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.

https://www.youtube.com/playlist?list=PLhQjrBD2T382Nz7z1AEXmioc27axa19Kv

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

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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
malan@harvard.edu