EM Algorithm : Data Science Concepts

[@aaroojyashin3790]

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

[@ritvikmath]

Glad it was helpful!

[@chainetravail2439]

@@ritvikmath I comfirm, thank you for helping lost students

[@soqjqxobfw]

Wow, this video is a real gem! It’s not just a simple breakdown, but really fills the gap that so many courses miss-explaining the intuition behind the EM algorithm, not just the math. This made everything click so much better for me. Thanks for such a clear and insightful explanation!

[@anuraganand9675]

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 :)

[@e555t66]

It would take a lot of time to develop these intuitions on your own.

[@rachelhobbs6189]

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

[@nnyabavictoria6332]

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

[@albertonieto2557]

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.

[@alizarean5080]

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

[@nawfalguefrachi2071]

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

[@ritvikmath]

Most welcome!

[@adrian-mu3jr]

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

[@DataRambling]

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

[@ritvikmath]

Wow, thanks!

[@vantongerent]

YES! I have quiz on this NEXT WEEK!

[@scottzgoldin2]

Wow, thank you for your work here. I finally feel confident in a subject in my masters classes and that means the world

[@aniketsakpal4969]

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

[@ritvikmath]

Glad it helped!

[@navyadewan8680]

your understanding and explanation of such a complicated concept is impeccable

[@michalistsatsaronis8728]

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

[@ritvikmath]

thanks!

[@andrashorvath2411]

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.

[@ritvikmath]

thanks for the additional inputs!

[@simeonvince8013]

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 !

[@peterhopkinson3040]

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

[@fotiskamanis8592]

Thank you! By far the best channel for providing clear explanations to fairly complex problems.

[@gergerger53]

I had a jolt of excitement when I saw you had decided to do a video on this topic. It's something I've had to revisit time and time again, always understanding the intuition, but always getting lost in the formulas. Your post did a great job at helping to explain the intuition. I did struggle a bit with your non-conventional likelihood notation, though. That did throw me off a little bit but I understand why you had to have it that way and quickly adjusted. The care you took in explaining why there is mu and mu0 just shows why you are a fantastic teacher.

[@aidanheffernan652]

It's 4am and I saw this video and had to watch....... really great explanation bro.....your a natural teacher.....thanks for this......subscribed

[@louighi91]

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

[@sakshamsingh2005]

By far the best explanation, amazing.

[@christophb9616]

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!

[@bassoonatic777]

Much better explanation than what I normally see. I would also be interested in seeing you go through the proof.

[@paramjeetsingh3444]

cant express how happy i m to see after yr videos . thanks a lot !

[@mohammed2250]

Yes please go on with the prove, that will be an interesting topic. Though I went on Andrew's ng video couple of times, but I couldn't understand it better than here!! You're a rock star in simplifying complex concepts!!

[@abhishekganapure6456]

The only explanation you need for understanding EM algorithm, proper chad explanation!

[@pouce902]

you are just amazing! What would be super useful would be an EM video based on your "Maximum likelihood" one.

[@ericwilson8665]

Absolutely fantastic. I agree w/ other comments... The DS world needs to see this. Thank you.

[@ritvikmath]

Glad you enjoyed it!

[@chaurasiaansh6059]

this is the best lecture for em algo

[@tb1131]

Your explanations are soooo clear! really appreciate the effort you put into your videos. Thank youu!!

[@thetrainoflife8327]

Genius man, genius , Wonderful explanation !!

[@MN-zs8lc]

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

[@sasakevin3263]

Awesome! Best explanation of EC algorithm for the beginner!

[@ritvikmath]

Glad it was helpful!

[@H1K8T95]

Ngl my favorite rapper-turned-algorithm

[@evgenyivanko9680]

Ritvik, you are doing a great job, thanks

[@sajedehaghababaei5300]

Thanks a lot! That is a great explanation!!! I was struggling with EM for a long time!! :)) I'd grateful if you also talk about the proof of convergence!

[@harry_rotter]

great video! Thank you, finally an understandable video.

[@ehsanmon]

Thanks so much for this great and explanation! I would definitely be interested in the proof. It will be great if you could do a video on Gaussian mixture models as well and how it is solved using the EM algorithm.

[@sharmilakarumuri6050]

Very well explained

[@JanSchmid-d4k]

Thank you for all the work you put in your videos to make life's like mine easier. Cheers man!

[@juliachu4685]

THANK YOU. You're literally saving my ML undergraduate course

[@ritvikmath]

awesome!

[@mihirsalot]

Loved it. Thanks for the efforts.

[@nuamaaniqbal6373]

i thank GOD i found your channel. A big thanks to youtube and to you!!

[@fabianomenezes5892]

What an amazing channel, honestly

[@yulinliu850]

Excellent. Thank you so much! 👍

[@ritvikmath]

thanks!

[@biswajit-k]

Got this crystal clear. Thanks a lot!

[@sgklee2664]

This explanation is amazing in order to get the concept

[@hannahnelson4569]

Very cool! Thank you for teaching!

[@tecnom7133]

The best Thanks man

[@srinivasbringu67]

Lovely, that's very intuitive. Thank you so much.

[@TanmayGhonge]

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

[@hangsu5724]

A real master can explain the most complex problem in an understandable way.

[@commentor93]

Thanks for your explanation. I think my main mental knot was wondering why you alter N instead of looking for the best guess for x to locate the value of the unknown value. To realize that x doesn't change and the power of the algorithm lies in finding the optimal solution for the learner without caring for the actual value of x was what I needed for it all to make sense.

[@moravskyvrabec]

Wonderful. Definitely helps my understanding. When I find time I want to see what you're doing w/ the stock predictions. If I remember lectures from business school, you should not be able "generate alpha" unless you possess information the market does not. In this case you could say you've found some new idea that has real predictive value, but either they will a) already have found this and put much more compute + their proximity to the actual place where the trades happen towards getting the answer first and beating you to the trades or b) didn't know it before but will immediately steal it and then see a) haha. But hey, I'll still watch to see what you've got going on.

[@MegaNightdude]

Great video !

[@bradleyadjileye1202]

Amazing, thank you for that !

[@ritvikmath]

Glad you liked it!

[@sahilagarwal1871]

Would love to see a proof video! Keep up the great work!

[@dadimanoj9051]

Thankyou for explaining very clearly

[@awangsuryawan7320]

Can't wait to see the proof

[@yaochung-chen]

Really nice explaination! Thank you!

[@ritvikmath]

Glad it was helpful!

[@vijayrajan5792]

Very compelling ... Brilliant

[@nikosp3903]

Amazing explanation!

[@isciaberger5017]

this helped so much, thank you a lot!!

[@parsamomemi]

you nailed it, very beautiful

[@tungbme]

Thank you so much for your explanation, helps me a lot

[@timchang5676]

yeah this guy is the fucking goat

[@QuocHuyPham-s4n]

Excellent explanation!!

[@bbcailiang7]

Excellent explanations!

[@davidforman4080]

So clear -- wow!

[@sneggo]

Great video! Thank you so much

[@ritvikmath]

Glad it was helpful!

[@taxtr4535]

You are a gem

[@user-wr4yl7tx3w]

This is explained so well

[@kulbhushansingh1101]

great man, ultra great

[@apresunreve]

amazing, thanks for such a clear explanation :)

[@nitishyamsukha]

very good explanation!

[@ritvikmath]

Glad you think so!

[@neelabhchoudhary2063]

oh my god. this was so helpful

[@ritvikmath]

Awesome!

[@skanoun]

Great teacher❤

[@lordscourge-jp8ch]

Thank you so much BRO

[@oliviernussli3799]

really great explanation! thank you :-)

[@RahulSingh-be2mg]

Incredible

[@kaurnavjeet943]

Amazing.

[@ritvikmath]

Thank you! Cheers!

[@vincentmair3880]

i litterly love you

[@n.m.c.5851]

thank you !!!

[@pallavibothra9671]

Hi Ritvik, thank you very much for awesome videos. Could you please make some videos on SQL?

[@ritvikmath]

thanks! and please check out my full SQL playlist here: kzbin.info/aero/PLvcbYUQ5t0UFAZGthysGOAtZqLl3otZ-k

[@pallavibothra9671]

@@ritvikmath Awesome! Thanks a lot.. Could you please add sql with window function to the playlist, if possible?

[@dannyzilberg3101]

Thank you so much for these videos! One question: how do you estimate and maximize the integral in practice? That was the elephant for me...

[@Martyr022]

I'm interested in the proof!

[@dougcree6486]

A worked example of the final process would be invaluable.

[@Thefare1234]

Great explanation. However, the way you have written it, there is no difference between the likelihood function and the probability function. I think for clarity you should swap x,1,2 and \mu. Also you should use ; instead of | so that the likelihood function is not confused with conditional probability.

[@Tyokok]

Thanks for the great lecture. One question if I may: 2:20, why you put best guess 1 here instead of a random draw from your known distribution?

[@kevinshao9148]

Thanks for the great video! One question: if you have (1+2+x)/3 = x , then you can have close form solution, why you still need numerical approach?

[@deepshikhaagarwal4125]

Thanks So much Ritvik!Your videos are amazing...do you have list of playlist for machine learning to connect dots in ML concepts,I see playlist for data science but not for machine learning. Thanks

[@sairaamvenkatraman5998]

Coild you please do the derivation or intuition for EM for clustering? I observe that it is described in many textbooks, but not in such a cool way. 😅

[@eceserin]

Example with python coming anytime?

[@akshiwakoti7851]

Great videos. Got it in one go! Could you do Gaussian Mixture Models? Thanks.

[@mohammadrezanargesi2439]

Something does not seem right to me in the E-step. I think the likelihood should be written given the latent variable, which is "x" in this case. But you have written it in given mu... I'm confused. Also I don't understand how to solve the M-step. When i write it down in this case i cannot update x at all🤦🤦🤦 I only update mu 🤦🤦 I'm completely confused

[@nipa161991]

Thanks for the video! What was not clear to me is whether we calculate all E(LL|M) for all Ms in which we can calculate the argman in step 3?

[@sharmilakarumuri6050]

Can you do the proof too please

[@xiaogangcho6261]

Thanks a for the easy-to-understand intuation of EM algorithm. Would you like to explain the Coin-flip example along with your formulation step ② and ③?