EM algorithm: how it works
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- เผยแพร่เมื่อ 27 ก.ย. 2024
- Full lecture: bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.
I am a PhD student. I have seen the material in three classes and had no idea what was going on. You made it crystal clear in 8 minutes.
You must of had comp stat with Tony too lol
I don't mean to be rude but what area of research do you specialize in? Because that's some pretty basic math here that they teach in your freshman or sophomore year in college.
@@Beowulf245 Very intentionally rude. I've had the same issue, and without solid intuition or clear explanations like this video, the math looks like a confusing mess that would go right over a lot of undergraduates' heads.
@@Beowulf245 I have two masters degrees in Astrophysics and Computer Science and currently a PhD student in computer science and I needed a refresher on EM and was failing to find anything useful online and couple of book chapters I went through were not enough for me to grasp the concept on a practical level of implementation for my problem right now and this video helped. So…
im doing this shit in my Bachelors and it's kicking my ass. i guess we all united in bad teaching of this topic regardless of our level
I was struggling with EM for a long time. You explained it very simple. I believe this is a kind of art if some one explains sth hard in a easy way. This makes different. I appreciate your help.
It's also an art when someone explains something quite easy in a very hard way. I know a few Picassos in this genre
This helped me a lot during my exam for Modern Applied Statistics! I got a bit lost computing the derivations and their parameters with the Gaussian.
Omg. U don’t know how long I have struggled with this algo. Such a nice explanation!!!
Omg, now I know the intuition behind the EM algo. This is art and you are fantastic! They say if you can’t explain some concepts in an easy way, you don’t know it well enough. I guess that’s why you can explain this so clearly and why many teachers can’t. Thank you so much!
I like how you explained causation in parameter -> which group or which group -> parameter but we have none of them. It is truly a beauty to recognize this nuance.
What a great and clear explanation! I watched a few videos on EM/GMM and didn't quite get it as well as I do now. Your explanation of chicken and egg problem and the intro before that really makes it so much more intuitive. Thanks!
In our lectures they straight jumped into 2D examples and it was very hard to comprehend the formulas, it helped me a lot that you explained it with a 1-d example. Thank you very much!
I'll have to say, I looove your lectures. I watched your Decision Tree lecture 2 days ago, today I was looking for EM lecture, and you explain them all through. Thank you so much for sharing
The best introductory video to EM by far. thanks
Sir you are my hero! You always set up things so intuitively.
Hello Prof,
The breakdown of complex algorithms in simple steps is excellent.
Amazing video, perfectly explained the concepts without getting bogged down in the math/technical details.
youtube has opened doors for so many people who would not have otherwise gotten such a good lectures.
I'm a russian student, 2nd year. I've seen 3 lectures in russian language, but only now I get this algorithm. This is professionalism I sure
Thank you sir
Wonderful explanation. I've been watching my class presentations over and over with no result. Now I get it. :)
This is my first time to leave a comment!!! you are awesome!! I come from NYU. I am struggling my final project. It uses EM. You really save my time!!! This is video is wonderful!!!!
Awesome video...Now finally my concept of EM is clear...Nobody can make me understand the way you did..So many thanks.....Waiting for more interesting videos on machine learning...U r simply awesome..hats off
Rarely comment, but this was a fantastic video. Very few youtube videos on a subject as small as EM are this informative.
Thanks Victor, you really have a great educational style. Please keep making videos explaining important topics in data science in a clear way--I will keep watching them :)
Bion Howard Thank you for the kind words. Really happy you find these videos helpful.
I love this kind of explanation. Simple practical example that anyone can understand without tons of abstract mathematical expression. It helped me to understand the concept and then it's much easier to go deeper. Wish more teachers teach this way.
Thank you!
We need more of these sir.
Thanks, I'm working on getting more uploaded.
Best lecture on EM on youtube. Well done!
Superb and simplified way to explain the essence of GMM and EM..!
impressive! you made it sound so clear, and appreciate how you compared it with k means
Very helpful guide! I spent some time to read the paper and tutorial online and having trouble in understand the logic, and u just lead me to the point of understanding this logic in jst 8 minutes!
Seriously. I have a very rough time understanding my professor over zoom and his lecturing style doesn't help. This was easier to understand. Thank you.
This is by far the best and clearest explanation of the intuition of EM that I've heard. This is amazing. Thank you so much.
My god this was an amazing video. I think I've already commented on this before but watching it again was really helpful! Cheers
Thank you so much. This is the most clear explanation of EM I found. But the only question remained is: how to calculate P(a) and P(b) in P(b | xi) formula?
Subbed and will be going through your entire collection of videos. This video helped me take a step forward with my research. Thank you for your efforts!
this is so far the best explanation on this topic
very very eloquent.
Thanks for your time which you spend to teach
I was struggling to get a big picture of EM algorithm. What a great explanation you provided. Thank you. 😁
The comparison with K-means made it clicked for me. Thank you!!.
You just earned my sub
Thank you!
Very smooth explanation. Loved it!
Very clear and simple explanation. Fantastic. Thanks for this. Please upload more videos like these.
Thank you for the kind words!
Comparison with kmeans opened my eyes. Thank you
Thanks for this. Your video helped bring clarity to the problem statement.
BEST VIDEO ON EM ALGORITHM
Great EM algorithm explanation!
How do you calculate priors?
Great video Victor. Very simple to understand. Thank you for the help
Hello, How to update the existing GMM when new data come?
at 3:08 the variance estimator shoud be divided by (nb-1) as corrected estimation and not nb .. that's what we call Bessel's correction
Your voice is like "Gale Boetticher" the lab guy from Breaking Bad, good video though
At 5:37, how do we know P(b) and P(a)?
The way of explanation is very good.
Awesome explanation !!
Thanks for this amazing video! It clarifies EM and it's really helpful! Thanks for making it!
Clear, concise and insightful. Thank you.
This is crazy. You explain it so clearly!! Thanks alot!!
so grateful to you man for this explanation!
At EM, do we know how many sources? Or we have to guess that as well?
At 5:25, P(x_i|b) equals a PDF as stipulated by your equation. However, since the random variables are continuous, shouldn't the likelihood equal a density function f, NOT a probability? You'd need to integrate over some interval in order to claim the Gaussian PDF equals a probability
How do you calculate the new parameters in EM? In k-means you would compute the means for each feature, but I'm confused about how you do that in EM?
Nvm I found your next video explaining exactly what I was asking. Great videos
Thanks so much! I will refer my students to your webpage!
This is so easy to understand. Thank you.
thank you for this lecture, extremely well explained
Great explanation, thanks!
Beautifully explained.
Such a nice explanation!!!
thanks man, this video saved me so much time to understand it
Thanks, glad this was helpful.
Hello, please can you show me the reference where i can find the membership cluster expression of expectation maximization? I will be very grateful for you help, thank you
Is it possible to use the EM technique with categorical variables and if so, how does the technique change to do that?
Best explanation. Thank you very much.
AWESOME EXPLANATION, THANK YOU
Very very good explanation! Thank you!
Really well explained .. which book is used in the course ?
Thank you. The lectures aren't based on any single textbook.
Thank you so much for this great video!
@2:40 the expression should be (x1-mu_b)^2 instead of (x1-mu_1)^2
great video, the only part that I found confusing was the last sentence. it will re-estimate to fit the assignments a little bit better. if P(xi|a)=0.18 and P(xi|b)=0.17, is it going to try to maximize just one of them or a combination or both or what?
ah, nevermind, I watched your example video, it is now perfectly clear. th-cam.com/video/iQoXFmbXRJA/w-d-xo.html
Great explanation!
Thank you for the video, it helped me a lot!!!!!! Great explanation
Thanks, very helpful! My course notes are stuffed with math notation. This is much better.
You´re amazing
Hats off to you sir.
Really it is very simple to understand.Good one :)
Would it be possible to do Kernel Density Estimate on the raw values, build up a bimodal smoothened distribution, then use that to find the underlying distributions? Maybe that's already done and has a name, i'm just not very good at statistics ^_^
Thank you so much for this lecture!
Well done! Thank you so much!
this is awesome! really helpful!
this video is legendary!
so helpful ! love your videos !thanks !
Thanks for the video! It was really helpful :D
Thanks, great explanation!
Great video! Thank you very much.
Outstanding!
great .....sir .....hats off for you .
Thanks a lot! I'm going to the finals in 2 days so if I draw EM as my question (which I hope for now), I will use this example :-)
Thank you! I hope you do well on your final.
Thanks! I did, eventually I drew Bayesian and Non-Bayesian tasks, but I think I would have made it with EM too :-)
Excellent!
This was helpful, thank you.
I love you. You save my day.
Could you please share the slide
Very clear!!!! Thanks so much!!!!
Nice explanation. Thanks a lot
You're welcome.
omg it was so useful! THANK YOU!
great lecture, tks
Great, that was what what I was looking for.
Thanks!
Is EM similar to Fuzzy C-means algorithm? It would be really helpful if someone could help me.