undergraduate machine learning 9: Hidden Markov models - HMM

[@jmcarter9t]

Nando's lectures are certainly some of the best on the web. Mastery is step one. Communicating that mastery is a real gift!

[@htetnaing007]

Don't stop sharing these knowledge for those are vital to the progress of humankind!

[@blaznsmasher]

Amazing! I've watched a couple videos on HMM before this and this is by far the most clear and easy to understand

[@qorod123]

Having teachers like Prof. Nando makes me fell in love with science. Thank you so much Professor.

[@jmrjmr8254]

Great! Now this paper I'm reading finally starts to make sense! Most helpful video on this topic !

[@azkasalsabila5328]

The best lectures I have ever watched on KZbin!!! Great professor. The explanation is easy to follow. Thank you.

[@mehr1methanol]

Very very helpful!! Unfortunately when I joined UBC you were already gone to Oxford. But I'm so glad you have the lectures here.

[@OmarCostillaReyes]

Great presentation Nando. you succeed on teaching excellence on this lecture. you made you presentation interesting, funny and knowledgeable. Congratulations!

[@wackyfeet]

I am so glad someone told him to fix the colour on the screen. I was losing it hoping he would fix the issue.

[@23karthikb]

Fantastic explanation Nando - great lecture! Thank you!

[@tzu-minghuang7100]

Great video for understanding HMM, worth every minutes

[@ThuleTheKing]

It is by far the best presentation of HMM out there. However I miss an example going from x0 - x2, so both cases (initial and t-1) are illustrated. It could also be nice, if you uploaded the assignments and answers.

[@nargeschinichian6286]

He is an amazing teacher! I suggest you to watch his class!

[@ilnurgazizov2959]

Excellent! A great and clear explanation of HMM!

[@fisherh9111]

This guy has a great sense of humour.

[@noorsyathirahmohdisa2720]

Best explanation of all. Tq you helped my research on speech recognition

[@user-eh5wo8re3d]

Really nice lecture. Very engaging and informative as well.

[@MrA0989741818]

Very good lecture!!! Thanks so much for saving me a large amount of time!

[@hsinyang1796]

I'm at student at UBC, we need you back teaching this course :

[@asharigamage7486]

Very clear explanation and best examples from GOT ..Thank you so much :-D....

[@_dhruvawasthi]

At 43:22 why it is outside the markov blanket?

[@DrINTJ]

Most lectures seem to go in length and repeat ad infinitum the obvious parts, then jump over the important bits.

[@pandalover555]

"You were so happy to see those dragons" LOL this guy is hilarious

[@riyasamanta3236]

simple..easy to understand..real world problems..thank you for this video.Can you upload more about the applications and extension of HMM ??

[@PoyanNabati]

This is fantastic, thank you Nando!

[@ar_rahman_90]

Thankyou so much for these lectures!

[@qureshlokhandwala3866]

Amazing intuitive explantation.Thanks!

[@doggartwuttus1082]

Thank you! This was really engaging and helpful.

[@acltm]

Hi- could you also tell how to estimate the transition matrix?

[@arnabpaul6996]

Me Before this Video: X(0): Sad Y(0): Crying Me After this Video: X(1): Happy Y(1): Watching GoT Me After Watching GoT: X(2): Sad Y(2): Crying, because the last season sucks

[@ivansorokin8054]

If node x0 do not have y0 on the graph then we assume P(x0/y0) = P(x0) when predicting P(x1/y0) or we don't count transition between x0 to x1. I think graph must have node y0 and we can use Bayes rule to compute P(x0/y0) like in the beginning of the lecture. Anyway, thanks for sharing lectures.

[@amizan8653]

Thank you very much for posting this!

[@phillipblunt]

Really fantastic lecture, thanks a lot!

[@saijaswanth5085]

Can i know reference book of above lecture?

[@yuvrajsingh-wn3up]

If the events W,S,C and F are not mutually exclusive then what changes we need to make to present HMM?

[@IndrianAmalia]

thanks for the amazing lecture ! helps me alot :)

[@ghufranghuzlan4404]

omg the best explanation eveeeeeeeeeer .very helpfull thank u sooo much

[@Michael-kt3tf]

Just wondering since we just care about the posterior. Why the forward algorithm compute the joint distribution? What is the point of that?

[@AnekwongYoddumnern]

Dear sir, if I use pir sensor with markov chain how many state that I should to setting?

[@noorsyathirahmohdisa2720]

Where to find his next video on HMM?

[@ryuzakace]

were you able to find it? It seems the HMM part is missing in this lecture, there is a slide present though which is not covered in this lecture

[@allisonzhang6527]

awesome! thanks ,Nando!

[@youssefdirani]

I didn't know the smoothing assignment ... What was it ?

[@aashishraina2831]

I loved teh material. Thanks a lot

[@shashanksagarjha2807]

someone please let me know.. can HMM be used for anomaly detection.. if yes . does it work better than techniques such as SMOTEENN and wighted class

[@yuezhao8657]

I do not feel either HMM or smote is a major anomaly detection technique. The more common approaches are LOF, Isolation forest, ocsvm, abod, loci and so on.

[@shashanksagarjha2807]

@@yuezhao8657 as far as i know LOF, isolation forest works better in case of unsupervised learning but technique such as weighted class or smoteEnn work better when we have labels. How much accuracy we can get in HMM

[@ddarhe]

at the beginning of the lecture; shouldnt the columns of the table add up to 1 instead of rows? P(y|x) + P(y|~x) = 1, right?

[@gggrow]

No... P(y|x) + P(~y|x) = 1 Whereas P(y|x) + P(y|~x) means "the probability of y given x plus the probability of y given not x". That could equal more than 1 if y is likely in both cases, or less than 1 if y is unlikely in both cases

[@gggrow]

So... P(sad|crying) + P(sad| not crying) doesnt have to equal one because maybe I'm not likely to be sad either way, but P(sad|crying) + P(not sad| crying) = 1 because that exhausts the list of possible states; I have to be either sad or not!

[@charliean9237]

That's what I thought too. Summing rows to 1 means this puppy always does one of the 4 things, and the puppy never eats. However, summing cols to 1 means the puppy is either happy or sad, which makes more sense.

[@upinsanity]

absolute masterpiece!

[@gopalnarayanan4217]

very good explanation

[@shineminem]

OMG this is so helpful!

[@ralphsaymoremakuyana7126]

great. well explained!!

[@kunwaravikalnathmathur2003]

This video has baye's theorem applied in full form

[@RelatedGiraffe]

6:37 We are all gonna be there one day? Speak for yourself! :P

[@bhagzz]

Really good one :)

[@pebre79]

wow. thanks for posting!

[@engomasri]

Thanks so much !

[@roseb2105]

i dont understand the equation

[@abcborgess]

brilliant.

[@tina3829]

Super!

[@WilliamStevenDonald]

very good

[@samidelhi6150]

YuePeng Guo why

[@dr.loucifhemzaemmysnmoussa7686]

Great!

[@yatmoparni993]

Dr. Loucif Hemza Emmys n Moussa

[@nickizcool20]

HMM 🤔