Maximum A Posteriori and Maximum Likelihood Estimation

Recall that learning from data given a model class f involves finding a good set of parameters. How should we do this?

Intro to MAP (maximum a posteriori) and MLE (maximum likelihood)

Covers:
0:00 Recap
0:25 Definitions of MAP and MLE
1:42 Names of probabilities in Bayesian Inference
2:36 Relationship between MAP and MLE

Please let me know about any improvements or corrections that can be made to the video.
Any opinions are solely my own.

Sources:
Wikipedia license:

Icons:
Icons8 License:

Music:
Creative Commons Attribution 4.0 International (CC BY 4.0)

[fyve]

Hi Ryan, I'm still confused about why they dropped the denominator that has a constant value.. can you give me an example to prove that it doesn't matter to drop the denominator?