Language - Lecture 6 - CS50's Introduction to Artificial Intelligence with Python 2023

00:00:00 - Natural Language Processing
00:05:19 - Formal Grammars
00:13:19 - n-grams
00:16:56 - Markov Chains
00:19:09 - Naive Bayes
00:31:13 - Word Representation
00:35:40 - word2vec
00:48:38 - Attention
00:54:15 - Transformers
01:03:30 - Artificial Intelligence

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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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

David J. Malan

[jotablanco]

In an internet world full of clickbait and low quality information there is also a place for top world educators providing greatly prepared content on state of the art knowledge! Thank you Brian and everyone involved for your amazing work! Thank you for making it free access!
And congrats fellow students on making it to the end!

[rolandlochli]

The whole course was exceptional! Maybe the best of its kind. Many thanks to Brian and the team behind it for creating this! I study Political Science and Linguistics, but now I want to start programming in Python. I know that it's a huge step from programming to artificial intelligence because the transtion requires a lot of mathematics. These lecures provided a great overview of which aspects of mathematics are most relevant.

[wohola]

Thanks Brian Yu and Harvard University for this wonderful course. I have finished it with a feeling of accomplishment. The course is easy to follow, yet the wisdom in it is so profound. Thanks you again!

[jeremyzhou]

Thanks for all your hard work, Brian and everyone who used to be involved into this course.

[rezigelenidze]

Thank you for being my satellite since IX Grade when i first saw CS50T & CS50x. This quality, this self-learning approach made my path to computer science. As a CS Sophomore still watching Harvard lecture, amazes me the same way with quality of explanation as it was for me 5 years ago - It's so nostalgic. Thanks for that inspiration and impact on my journey. This is CS50, Respect from Georgia 🎓🇬🇪

[sadtomatogirl]

I'm a training developer at a cloud company with no computer science background, and I found this course tremendously easy to follow, interesting, helpful, and useful. Brian very clearly explains the basic concepts and functions, builds upon concepts, and uses detailed, helpful graphics to explain them. I can't imagine how long it must have taken to develop all of the slides and animations, which are animated and so perfectly timed. Great course -- thank you!!

[inhmanh]

Thank you Brain and everyone involved in making this course

[foo_tube]

This class is so excellent. I wish it would just keep going and going further.

[Jack-vvzb]

congrats everyone on making it to the final lecture!

[atr]

Thanks you to everyone involved! An exeptional course!

[cswiz]

Finished the whole playlist and I WAS NOT BORED! Thanks once again to Brian and CS50 team <3

[evachen]

Thank you Brian and all the CS50 team members ❤
The class can’t be so excellent without any of you guys, and it’s so good that I am still brainstorming and planning to review the whole courses again. 😅

[l_sx]

To anyone who has made this far in the course, well done, you deserve as much compliment as Brian and Harvard for making this course.

[Laowater]

Extraordinary work!

I bow down to you and your work, Brian.

I bow down to you, Harvard.

This has been one of the greatest programming experiences ever lived!

A long journey of 5 months with thousands of pages written and un-measurable knowledge and skill base increase.

The Art of Programming.

I Shall always hold close to heart the ending of CS50: CS - about realising the great Power of programming - and the use of it with Responsability, care and purpose for this beautiful, beautiful World.

[khangvutien]

I agree with all the comments. Thanks a lot.
This course is a gem.
Not the least of the achievements is to do such neural network training without a single piece of maths. 🙃

[Mshahnawaz]

So that was the end for this course, it was very enjoyable and i got to learn many new things. Thank you cs50 team for bringing this course.

[Guitarcoverandlessons]

Big thanks for this amazing course, I enjoyed it to much. It was funny to do projects and listen the lesssons. Many thanks to Professor Brian and the team behind it for creating this! It's wondefull

[Aypy]

Bro i was so happy that i am completing the CS50 AI until brain said "we literally just scratched the surface of A.I "

[nboston]

Thank you so much! This course vastly improved my python skills and gave me a much better appreciation for application of AI

[gauravrawat.]

00:02 Computers understanding natural language.
02:26 Understanding the structure and ambiguity of natural language is crucial for computer understanding.
06:42 Nonterminal symbols can generate terminal symbols
08:58 Using NLTK to parse a context-free grammar
13:30 Using n-grams to analyze text structure
15:38 Understanding n-grams and tokenization in natural language
19:38 Text classification and sentiment analysis are common use cases of natural language processing.
21:26 Using bag-of-words model to build Naive Bayes classifier
25:24 Estimating conditional probabilities based on training data
27:19 Using normalization to predict sentiment from text
31:10 Language data can be utilized by representing words as numbers for neural network processing.
33:00 One-hot representations have limitations
37:09 Word vectors in language processing
39:11 Word2vec captures relationships between words as vectors.
43:21 Neural network runs maintain hidden state between iterations.
45:17 Using a recurrent neural network to encode and decode input sequences.
49:08 Attention mechanism helps calculate the importance of words for generating output words.
51:17 Attention is a powerful tool for determining important input words for generating output words.
55:12 The Transformer allows processing words independently and in parallel
57:10 Using positional encoding and self-attention to represent word meaning
1:01:10 Attention steps allow words to pay attention to each other
1:03:11 Understanding natural language is crucial for effective AI agents.