Braveness to Be taught ML: Decoding Chance, MLE, and MAP | by Amy Ma | Dec, 2023
With A Tail of Cat Meals Preferences
11 hours in the past
Welcome to the ‘Braveness to study ML’. This collection goals to simplify advanced machine studying ideas, presenting them as a relaxed and informative dialogue, very like the partaking type of “The Courage to Be Disliked,” however with a deal with ML.
On this installment of our collection, our mentor-learner duo dives right into a contemporary dialogue on statistical ideas like MLE and MAP. This dialogue will lay the groundwork for us to achieve a brand new perspective on our earlier exploration of L1 & L2 Regularization. For an entire image, I like to recommend studying this publish earlier than studying the fourth a part of ‘Courage to Learn ML: Demystifying L1 & L2 Regularization’.
This text is designed to sort out elementary questions that may have crossed your path in Q&A mode. As all the time, if you end up have related questions, you’ve come to the correct place:
- What precisely is ‘chance’?
- The distinction between chance and chance
- Why is chance vital within the context of machine studying?
- What’s MLE (Most Chance Estimation)?
- What’s MAP (Most A Posteriori Estimation)?
- The distinction between MLE and Least sq.
- The Hyperlinks and Distinctions Between MLE and MAP
Chance, or extra particularly the chance operate, is a statistical idea used to judge the chance of observing the given knowledge below varied units of mannequin parameters. It’s known as chance (operate) as a result of it’s a operate that quantifies how probably it’s to look at the present knowledge for various parameter values of a statistical mannequin.
The ideas of chance and chance are basically totally different in statistics. Chance measures the prospect of observing a particular consequence sooner or later, given recognized parameters or distributions…
