Nonparametric Bayesian Methods: Models, Algorithms, and Applications I

Tamara Broderick, MIT
simons.berkeley.edu/talks/tam...
Foundations of Machine Learning Boot Camp

[@terryr9052]

The guy constantly adding info from the audience is annoying

[@michalelee9107]

i heat to be interrupted by audience

[@jansensun6191]

Explaining a simple idea in a complex way is not so great.

[@batuhankavlak]

Is there sth that explains notations used in the slides? Every time I watch sth about a topic, all notations differ; and the speaker may forget to clarify. A clarification as notes would help the audience a lot!

[@pattiknuth4822]

Apparently she took credit for something someone else discovered

[@timgraham1785]

The blokes in the audience interrupting her lecture... I mean, come on

[@muratcan__22]

perfect intuition at the beginning about the effects of beta and dirichlet parameters

[@amirkhan355]

What a great teacher

[@ejaz629]

Great talk!

[@pauloabelha]

Great lecture!

[@jakubpruher979]

So far the best explanation of DPs I've seen to date!

[@horacetso]

the illustration on sampling from Dirichlet distrib is probably the best I've ever heard (starting

[@richasrivastava999]

very insightful explanation on non parametric Bayesian.

[@yunzheli8784]

This is amazing.

[@mathom21]

It is just outstanding

[@chrisolen2113]

superb.

[@pauloabelha]

41:10

[@mtheory85]

32:40

[@user-bh8fp6wq8q]

Great lecture!