Learning - Lecture 4 - CS50's Introduction to Artificial Intelligence with Python 2020

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.

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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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CS50 SHOP

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LICENSE

CC BY-NC-SA 4.0
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License

David J. Malan

[johnpasir]

After the whole of CS50, and previous AI lessons, this starts now to getting "real" in the day to day sense. thanks😀

[kgaurav]

this is exactly what I wanted as an introduction to machine learning, beautifully simple explanations!! thanks so much :))

[gavranhas]

Thank you! This is great, high-quality content.

[makhmud_jumanazarov]

for awesome lecture, thank you, Brian

[peiyunlau]

I was doing a lot of the math with alpha learning rate but this is the aha moment! 25:34! A big thank you Brian!

[hichemfantar]

1:38:42 correction: categories of machine learning

[anitaafraz]

Your way of teaching is incredible, but I am accustomed to professor David J Malon.

[pkpprgv]

Question: (08:57) pressure and humidity have different units and it needs coefficients to make measuring 'distance' possible. And the result of the program may depend on how we scale those coefficient. How we can deal with this problems?

[UmbertoFontanazza]

The github of CS50 is huge, where can I find the nim library?

[jamil]

Isn't the k-means clustering algorithm a better approach for solving the hospitals problem in the optimization lecture?

[buzoonorana]

Where can I find the data of banknotes at 1:00:35?

[englishenlish]

Do I have to know incredibly well math to take those courses ? Please answer asap

[thongtranlequoc]

how could he explain these concepts like nothing 💀