Uncertainty - Lecture 2 - CS50's Introduction to Artificial Intelligence with Python 2020

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.

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LICENSE

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David J. Malan

[anthonydattolo]

thanks guys, this helped me with my homework so much. the quality of the CS50 team contributions to opensource continue to amaze me

[techguy]

Thanks CS50, probably the best cs entry course for me

[onkarsardesai]

Thank you so much for such a high quality teaching. I have never learned this topic with such a different point of view towards it.

[taianeramos]

Very good explanation! It's clear and easy to follow.

[hukunamutata]

Helped me so much with my assignment. Thank you <3

[yoloswag]

high school maths. why they don't include the rest of the probability computer models in high school, I DO NOT know. very well explained by this guy, thanks.

[avgerageCSStudent]

On 26:18, should the formula have an "equal" sign instead of "not equal"?

P(6 and 4) = P(6) P(4|6)

[antonionorris]

How do I get the joint table in 35:22?

[syedshorifulalamopu]

35:53 how was the joint probability of cloud and rain was calculated?

[mekala]

I think there is a mistake with joint table at 35:22. Don't we have to basically multiply the probability distributions? Joint table should be: P(cloud, rain) = 0.04 P(not(clod), rain ) = 0.06 P(cloud, not(rain)) = 0.36 P(not(cloud), not(rain)) = 0.54.

[lennart]

Why, at 38:21, is P(C, rain) equal to P(C, rain) AND P(notC, rain)? Wouldnt it just be P(C, rain) so 0.08? If I understand correctly P(C, rain) is the probability of clouds AND rain. Or am I interpreting it wrong?

[NikiGruener-psvy]

how does this markov chain in the python example work? When I calculate the values for the markov chainby hand they always turn out to be sun constantly? Can someone explain?

[rishikeshkumar]

In probability rules, aren't marginalization and conditioning exactly the same.
Or, the latter is expanded form of the former?
If there is any difference between those two, can anyone please explain why they are two different rules?

[Noe-gjmw]

I wish you hadn't forgotten to include requirements.txt in the source code like you usually do! because it means i have to pip install the libraries and I'm not fully sure which to install!

[arundoylenka]

Okay this may be way outside the topic but doea anyone know the intro song/tune of this video? Like it really gets me hooked into the lecture. 👀

[kristiankolumber]

I'm sorry but there's absolutely no way that 20% of people are bringing an umbrella to the office when it's sunny outside

[johngarcia]

Yow you need some water bro that is some mad skill at a young age

[annieangel]

Hey can anyone help me.today only i created account how to join video all

[vishweshpujari]

I am unable to install pomegranate. Can anyone help me please

[Tikrong]

Is there a mistake in joint probability table? (35:58)