EM Algorithm : Data Science Concepts

I really struggled to learn this for a long time! All about the Expectation-Maximization Algorithm.

0:00 The Intuition
9:15 The Math

[aaroojyashin]

This could be by far the best explanation I have seen for EM algorithm. The way you have connected the intuitive way to mathematical explanation, is so so commendable!!!! Thank you so much for your efforts

[anuraganand]

The world needs to see this. Thanks Ritvik, I honestly have utmost respect and love for the amount of hard work you put in your videos. Cheers :)

[rachelhobbs]

Your channel and way of teaching is so amazing!! Very inviting, inclusive, and friendly. Thank you so much for such good vibes 💕

[nnyabavictoria]

Thank you Ritvik for simplifying EM algorithm like this. This is the best video I have seen so far.

[simeonvince]

Thank you for the high-quality contents that you have produced over the past few years. Most of the time, it really did help me get the intuition and understanding of what was going on with the theoretical concepts I was seeing in my courses.
Once again, thank you !

[adrian-mujr]

That's really great way to look at EM. I'm an engineering graduate but new to ML and the workup explanation before dropping into the maths is excellent. thanks

[michalistsatsaronis]

Thanks!You explained such a complicated subject so clearly!!!!

[navyadewan]

your understanding and explanation of such a complicated concept is impeccable

[alizarean]

I have an exam tomorrow and this video was the thing I needed. I can't thank you enough dude.

[andrashorvath]

Awesome explanation. I'd like to extend yours with my intuition regarding the E-Step: the first term p(x|m0) shows the probability of x happening for the chosen m0, and the second term LogLikelihood shows the probability of x happening for the computed m, and we want to maximize both. Because we want a choice with high probability from every aspect. That's why we multiply them together. Because the multiplication can weight between them. If one of them is small then the result will be small. It can be high only if both are high.

[aniketsakpal]

Incredible explanation! Was trying to understand the intuition behind EM for a long time! Thanks for the video! Keep Going!!

[rishi]

It would take me two more lives to be able to explain it this well to someone, kudos! Great job buddy!

[albertonieto]

Brilliant explanation. I especially appreciate you first providing the intuition of the method in the verbal explanation of the E and M steps. I struggled with the seeing the math first in other lectures until seeing your video. Thanks for posting this.

[peterhopkinson]

Your videos are unreal, simple explanations of complex problems its insane.

[sakshamsingh]

By far the best explanation, amazing.

[christophb]

Thanks for the very clear explanation! A follow up video on how the EM algorithm can be used in gaussian mixture models or bayesian networks would be awesome!

[MN-zslc]

Although there is more for fully understanding, I was able to gain the concept because of your video!

[nawfalguefrachi]

thank you ritvik the best videos are in this channel.
Very intresting way of teaching thank you from TUNISIA

[louighi]

Holy, i can't believe how good this video was :) thank you so much

[TanmayGhonge]

Broke down the most complicated algorithm in the simplest terms. Wow!