21. Probabilistic Inference I

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* Please note: Lecture 20, which focuses on the AI business, is not available.
MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
Instructor: Patrick Winston

We begin this lecture with basic probability concepts, and then discuss belief nets, which capture causal relationships between events and allow us to specify the model more simply. We can then use the chain rule to calculate the joint probability table.

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Skins Song says:

It's just such an amusement to listen and watch him articulate this probabilistic view of proceeding facts and assess it.

corey333p says:

Where is lecture 20? I want it.

Ewann Thomas says:

plz add lec 20….plz plz

Dong Zhou says:

This is awesome

JNS Studios says:


q zorn says:

great info, how does Patrick Winston find time to teach at this level? thanks.

OzgurBagci says:

44:34 fight club, lol.

Bobby Nazaris says:

Correction: Chain rule product: it should be P(X_n,…X_1) not P(X_1..X_n) I think! @Minute 28 in the clip.

Art Lenski says:

You can certainly get his axioms 1 and 3 from the Kolmogorov axioms https://en.wikipedia.org/wiki/Probability_axioms. Regarding his axiom 2, he probably meant that p(S) = 1, p(^S) = 0, where S is a universal set (set of all possible outcomes), and ^S is an impossible event.

Ramon Zamora says:

Great lecture.  The best explanation I have seen so far.

Guy Smith says:

I am constantly amazed by the MIT lecture videos. MIT professors seem to be gifted at making complex ideas easy to understand. 

sam kins says:

what happen to lesson 20 i notice it was ommitted

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