A friendly introduction to Bayes Theorem and Hidden Markov Models
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- เผยแพร่เมื่อ 26 มี.ค. 2018
- Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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A friendly introduction to Bayes Theorem and Hidden Markov Models, with simple examples. No background knowledge needed, except basic probability.
Accompanying notebook:
github.com/luisguiserrano/hmm - วิทยาศาสตร์และเทคโนโลยี
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Happy I found this video.. even though it was rainy outside
Happy I found this video.. even though there's a Corona lockdown :D
It's coincidentally rainy outside 😂
Based on previous experiance, because it is rainy at your side, i predict that you were probably not happy 😔
@@a7md944 Bob was more likely not happy, we are the hidden state - whats the probability that the lockdown was not justified and that people were dying because of lack of medical help instead of the illness.
Usually Bayes Theorem and HMM are nightmare to even researchers. In this video these nightmares are made like child's play. I'm highly thankful for this service you are providing to the academic community- teachers, researchers and students. Keep it up Luis Serrano and hope to see many more plays in future!!!
your are one of those rarest breed of gifted teachers
wow. perfect explanation . Even a kid can learn HMM by watching this video
You have just saved me, this was such a clear breakdown of Bayes Theorem and HMMs, and exactly what I needed at the 11th hour of a project I'm working on!
Thank you so much for this great video Luis. I am a Udacity alumni myself. I have watched & read many videos and articles on Bayes & HMMs, but your video by far is the best. It explains all the steps in the right amount of detail & does not skip any steps or switch examples. The video really helped solidify the concept, and giving the applications of these methods at the end really helps put them in context. Thank you again very much for your information & helpful video.
Beautiful work! It’s the most accessible introduction to Bayes inference I’ve seen. Great job! Please, keep them coming!
This is the best description of this topic I have ever seen. Crystal clear! True knowledge is when you can explain a complex topic as simple as this!
Man Bayesian Theory has been having me for Breakfast! Thank you for this tutorial!
I can't believe how you did it so clear and simple. gorgeous
I'm on a streak of watching your third video in a row and instantly liking it for outstandingly easy-to-understand breakdown of a quite complex topic. Well done, sir, I'll visit your channel in the future for sure! ✨
Being a teacher myself for long time all I can say is that this video is awesome! You have a talent my friend.
Your videos are amazing! As someone who hasn't looked at calculus in 20 years, I find these "friendly introduction" videos extremely helpful in understanding high-level machine learning concepts, thank you! These videos really make me feel like this is something I can learn.
Isn't this opposite of calculus? Discrete vs continuous functions.
The most exciting thing I found in your video is that most of them is a one-stop solution for dummies like me, without the need to go to other 100 places to find 50 missing info pieces. Many thanks !
Your video tutorials are a great breakdown of very complex information into very understandable material. Thank You. It would be great if you could make a detailed video on PCA, SVD, Eginvectors, Random Forest, CV.
Eigenvectors and SVD for sure.
@@jacobmoore8734 check out 3blue1brown's channel for the Essence of Linear Algebra. He explains that matrices are linear functions like y=f(x) or like a line 'y=mx', with y-intercept b=0. Eigenvectors are special inputs 'x' such that f(x) = kx, where k is some scalar coefficient (k is the eigenvalue associated with the special input x).
For certain types of NxN matrices, (the covariance matrix used in PCA for example) are super interesting because any point in N-dimensional coordinates can be represented as a linear combination (ax1 + bx2+...) of the eigenvectors. The eigenvectors form a 'basis' for that space. This is where SVD (singular value decomposition) comes in. SVD essentially asks "instead of just multiplying x by your matrix, why don't you decompose this task into 3 easier tasks?" Let's say your matrix is C for covariance. Then SVD says that C = ULU' where U is made up of the eigenvectors for columns, U' is the transpose of U, and L is a diagonal matrix with the eigenvalues.
Pretend we're doing y = C*x. Then first we do w = U'*x. This essentially represents x as a linear combination of eigenvectors. Said another way, you've changed the representation of point x from the original coordinate system to the eigenvector coordinate system. Next we do z = L*w, which scales every value of vector w by an eigenvalue. Some of these eigenvalues are very small and the result in z is perhaps closer to 0. Some of these eigenvalues are relatively large and upscale the result in z. Finally, when you do y = U*z, all you're doing it translating your scaled z vector back into the original coordinate system.
So SVD basically splits a matrix into 3 different operations:
1. represents an input vector in terms of eigenvector coordinates
2. scales each coordinate by an eigenvalue
3. represents the scaled result back in terms of the original coordinates
When you look at PCA (principal components analysis), you take your covariance matrix and decompose it to look at how much your eigenvalues scale the eigenvector coordinates. The largest eigenvalues correspond to the direction (eigenvector) of largest variation
Definitely eigenvectors! Please!
Yes, please, do that.
You may have already found some, this is an attempt by University of Calcutta, not so coolly done, but please see if it makes sense th-cam.com/video/C6fH5Nfoj40/w-d-xo.html
You are the best explainer I have found in youtube till now! Great work!
Thank you so much for this. I wish more educators were more like you.
I wish professors would just show this video in lectures... You are great at making these animations and your speech is perfect. Thank you!
Top notch and best explanations. You are taking complex subjects and making it intuitive not an easy thing to do !
Thank you so much for this video! I searched for hours, watched many videos, read many websites/ papers etc. but i never really understood what a HMM and the algorithms are and how they work. You explained everything from how it works to how to implement it so well that I got in 30 minutes, what i didnt get in hours before. Thank you so much!!
OMG! you are amazing! I consider myself as a information theory guy and should know this pretty well. But I can never present this idea as simple and easy understanding as you did! Great great job! I will for sure check around your other videos! Thank you!
Thank you Changyu!
It's impressing how simple you explain very complex issues! Thank you!!
You made it so ease for learners... Appreciate the time you are spending in creating the content!!
This is the best ever video you will find on HMM. Complicated concepts handled soooo wellll🥰
I wasted the whole day understanding HMM model by watching useless youtube videos, untill I saw this. Thank you so much for this video. It is so simple and so intuitive. So very thankful to you :)
Simply amazing! After quite a long time struggeling to understand HHM now I finally get it. Thank you so much!!
I have a midterm in 8 hours and this video is the only thing that's really helped me so far. Cleared up all my confusions during 8 lectures in 32 minutes. Thank you so much, from the bottom of my heart.
Thank you for your note, I hope the midterm went great! :)
Thank you so much! This video literally helps me understand 3 lectures in my machine learning class
best description about HMM, I had hard time to understand this topic, but your teaching keep me motivated for further learning.
Nice job! Best explanation by now. Explained 6 weeks of my class in 32 minuts!
This has taken me from 0 to 80% on HMM. Thanks for sharing
a beautiful combination of all the difficult concepts in probability in one video. great job.
This is the best video that explains HMM so simply to someone who doesn't have a computer science background. Godspeed to you
Thanks to your videos, I save a huge amount of time. Focusing on the intuition and mechanic allows an instant understanding BEFORE delving into the maths
Thanks so much for this! It really helped with a research report I'm writing. Clear and easy to understand and the pacing was excellent for being able to take notes.
In this video I explain what conditional probabilities are and I show how to calculate them in Excel and how to interpret them, using Solver to implicitly apply Bayes' theorem. Though in spanish, subtitles in english are available: th-cam.com/video/rxHd7td6Xo0/w-d-xo.html.
So great by using sample example to explain confusing yet very important topics! Appreciate your excellent tutorial!
Excellent, excellent.
Great job.
Your all videos enlighning to all academicians
Omg. You just replaced an entire dry, non-understandable book for bioinformatics! I can’t thank you enough! It’s so easy!
Your videos are very helpful and giving a good intuition of complex topics :) many thanks from Siberia
Hi Luis Serrano thanks for the clear explanations, your informal way to explain this material is the best for us as a student, even my professor in Machine Learning class recommend this video for learning the HMM introduction!
I am a first time viewer but with such kind of amazing explanations, I will always stick to your teaching, vow so nicely explained!
I took a probability class and did badly. After recently finding out I'd need to revisit it for machine learning, I was a bit concerned. Then I come to understand an algorithm for Baye's Theorem!! How incredible, thank you!!
Best explanation of Hidden Markov Models on the Internet. Well done.
Very comprehensive and easily understandable. Even though I get increasingly impatient to watch the whole thing, I still managed to swing the thumb up.
Amazing ... I just bought your book from Australia. Thank you for your time and effort!!!
Thank you for making this! Fantastic and easy-to-understand explanation of the topic.
As a feedback I would say your explanation is spot on .... A person with basic statistical knowledge can understand HMM with your explanation
my dad recommended i watch this, and i sure am thankful he did :D great video!
Really amazing video that breaks down Bayes Theorem for simple understanding. Thanks Luis
This is the best explanation of HMM i ever seen up to now!
Dude, thanks a ton for explaining this so simply
Very easy to understand using Bob and Alice and the weather. Thanks.
this example made everything crystal clear, I have an exam tomorrow on HMM. Initially, I was anxious but after this video I'm sure I can solve any problem.
Thank you very much, sir.
It was so nice with images! When you switched to letters, it was super clear how much easier it was to look at images!
A very nicely done and visually appealing video on a slightly complex topic. Thank you!
the best explanation on the internet. Thank you!
The best explanation of HMM ever! Very visual and easy to grasp. Enjoyed learning so much. Thanks!
Edit: Can you please do a friendly video on EM algorithm, too?
THIS IS REALLY GOOD!!! Informative and easy to understand.
And please do a video on the baum-welch algorithms.
Once again, no words to thank you!
Happy New year!
Thank you so much! Your explanation and the way you presented the concept, was so crystal clear. Loved learning it.
Excellent presentation. Simple to follow. I'll check out your book.
Well illustrated. Thanks for putting this together.
Loved it. You are a great teacher. I was blessed finding your video first so I didn't waste any time 🥰
What a clear way of teaching. You're a total Rockstar of teaching stats. Ok, let's do the Baum-Welch algo
I was going thru HMMs for robot localization and found this super clear explanation. Eres un fenomeno, Luis. Gracias!
Dr Serrano, I think you are an embodiment of Feynman in ML education! Thanks a lot!!
Great video! Thank you for spending time to make these videos.
Best video ever! Very clear and well explained!
Really hope to see more and more "friendly" videos from you ! Thanks a lot !
Made my day...I learned Hidden Morkov Model for first ever time n guess wht? It was damn simple to understand the way explained.
Great explanation, clear and simple
Very helpful and clear example and explanation. Thank you!
I was quite tensed when my supervisor pointed out to me that my master thesis should incorporate HMM. This video is my first introduction to HMM. You chased my fears away with your simple explanation and tone. Forever grateful
Similar situation here, I have a master thesis in anomaly detection, and using HMM is a candidate. I'm afraid it's much more complicated than this, but it sure made it look less scary
Very useful and easily,nicely explained. Very much benefitted from it. Thanks a lot.
Thank you so much for this beautiful explanation. learned about application of Bayes and Markov together ...Would happy to see more engineering application of these thermos..
Very good video! Simple examples make it very approachable and keeps it from being overwhelming
Very very good explanation, easily understandable by my old brain. Thank you.
Thanks for the straightforward explanation of Bayesian networks + Hidden Markov Models. Cool stuff! Very powerful.
I am a bio-organic chemist and we have a bioinformatics course which included Hidden Markov Model and your video helped me to learn the idea without immersing myself deep into mathematics. Thanks ...
This is hands down the best video on HMM.
Thanks alot! I came across your video while searching for HMM-explanation for my computational biology course, and it helped a lot to understand the basic principle :)
You did a better job teaching this than my MSc
Very nice explanation! visual and geometric! thanks again!
This video helped me a lot to understand these concepts and applications. Good job!
this is one of the best explanations of the HMM, it was very helpful to me, Thank you very much!
I really like your example. It really helps with the understanding.
Excellent video, i remember looking at this on wikipedia and just not having a clue of what it meant, you did a fantastic job of explaining it!
Best video of its kind on TH-cam.
Thank you very much for the video. It is well explained and you made me understand the concept in easy way.
Very clear explanation. Thank you for the help
This video is really useful for me to learn HMM as well as probability calculation with algorithms. The example is easy to understand. Thank you so much.
Outstanding explanation! Thank you very much
Nice video with clear explanation. I can see lot of work & heart put into making this video.
Great job, I got it immediately. Very good illustrations too, simple and to the point
Really liked the video. Was looking to understand HMMs for neuron spiking and things are much clearer now.
Great video. Easy to follow and well paced.
Thank you so much for share this video! It's the best explanation I found for this topic.
Very good lecture about introducing Markov Models.
surely the best video on HMM