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Stanford Class: Probabilistic Graphical Models

pgm-class.org

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Re: Stanford Class: Probabilistic Graphical Models

#2

Post by sabraham »

PGMs don't get the same sexy treatment that ML and AI seem to get in pop science articles, so it may be worth stating explicitly that they're very much used in ML and are intellectually fascinating in their own right. A graphical model can fully describe the distribution and dependences of a model. Why this is important: a graphical model makes it very easy to give a computer your model, and there has been great success over the past two decades in doing just this [1].

Further, Daphne Koller is a serious force in the field, and seems to be a pretty good supervisor, so I'm guessing/hoping she is an interesting/engaging lecturer as well. Though, Stanford CS/Stats students are more able to comment on this last point.

[1] http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml

Re: Stanford Class: Probabilistic Graphical Models

#3

Post by taejo »

sabraham wrote: post #2

PGMs don't get the same sexy treatment that ML and AI seem to get in pop science articles, so it may be worth stating explicitly that they're very much used in ML and are intellectually fascinating in their own right. A graphical model can fully describe the distribution and dependences of a model. Why this is important: a graphical model makes it very easy to give a computer your model, and there has been great succ…

I'm I right to surmise that "graphical" refers to graphs rather than "graphics", or was that just one example in the video?

Re: Stanford Class: Probabilistic Graphical Models

#4

Post by jey »

taejo wrote: post #3
sabraham wrote: post #2

PGMs don't get the same sexy treatment that ML and AI seem to get in pop science articles, so it may be worth stating explicitly that they're very much used in ML and are intellectually fascinating in their own right. A graphical model can fully describe the distribution and dependences of a model. Why this is important: a graphical model makes it very easy to give a computer your model, and there has been great succ…

I'm I right to surmise that "graphical" refers to graphs rather than "graphics", or was that just one example in the video?

Yes, "graphical models" are a formalism for representing and reasoning about probability distributions using graphs.

Re: Stanford Class: Probabilistic Graphical Models

#5

Post by cypherpunks2 »

sabraham wrote: post #2

PGMs don't get the same sexy treatment that ML and AI seem to get in pop science articles, so it may be worth stating explicitly that they're very much used in ML and are intellectually fascinating in their own right. A graphical model can fully describe the distribution and dependences of a model. Why this is important: a graphical model makes it very easy to give a computer your model, and there has been great succ…

> Further, Daphne Koller is a serious force in the field, and seems to be a pretty good supervisor, so I'm guessing/hoping she is an interesting/engaging lecturer as well. Though, Stanford CS/Stats students are more able to comment on this last point.

What's the source? She's a brilliant researcher, but I've heard quite the opposite about her attitude towards human relationships...

Re: Stanford Class: Probabilistic Graphical Models

#6

Post by gwern »

taejo wrote: post #3
sabraham wrote: post #2

PGMs don't get the same sexy treatment that ML and AI seem to get in pop science articles, so it may be worth stating explicitly that they're very much used in ML and are intellectually fascinating in their own right. A graphical model can fully describe the distribution and dependences of a model. Why this is important: a graphical model makes it very easy to give a computer your model, and there has been great succ…

I'm I right to surmise that "graphical" refers to graphs rather than "graphics", or was that just one example in the video?

Yes. Confusing, isn't it?

Re: Stanford Class: Probabilistic Graphical Models

#9

Post by sabraham »

cypherpunks2 wrote: post #5
sabraham wrote: post #2

PGMs don't get the same sexy treatment that ML and AI seem to get in pop science articles, so it may be worth stating explicitly that they're very much used in ML and are intellectually fascinating in their own right. A graphical model can fully describe the distribution and dependences of a model. Why this is important: a graphical model makes it very easy to give a computer your model, and there has been great succ…

> Further, Daphne Koller is a serious force in the field, and seems to be a pretty good supervisor, so I'm guessing/hoping she is an interesting/engaging lecturer as well. Though, Stanford CS/Stats students are more able to comment on this last point. What's the source? She's a brilliant researcher, but I've heard quite the opposite about her attitude towards human relationships...

Ah, so I used a 2-degree heuristic to come to that conclusion--I haven't had any first hand experience with her, nor do I have contact with her former students. A few stats professors independently recommended her to me as a supervisor, her students seem to do well, and her research page is more welcoming than most (versus, say Ullman's page: http://infolab.stanford.edu/~ullman/ or say, read Brian Ripley's posts on the R mailing list). The one thing I'd add: my experience has been that academics generally have less empathy than others; I'd be interested to hear from old students how she compares to other faculty.

Re: Stanford Class: Probabilistic Graphical Models

#10

Post by sabraham »

forkandwait wrote: post #8

I have the book, and it is fantastically well written and easy to follow. I just signed up -- does anyone know if they send an email or something?

The other classes sent an email when registration opened; I think it's safe to assume PGMs will be similar.
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