* 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.

License: Creative Commons BY-NC-SA

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

Where is lecture 20? I want it.

plz add lec 20….plz plz

This is awesome

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

44:34 fight club, lol.

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.

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.

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

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

what happen to lesson 20 i notice it was ommitted