Machine learning - Maximum likelihood and linear regression

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Maximum likelihood and linear regression.
Slides available at: www.cs.ubc.ca/~nando/540-2013/...
Course taught in 2013 at UBC by Nando de Freitas

Пікірлер: 27

@tonimigliato2350 5 жыл бұрын

I feel bad for people trying to learn Machine Learning and don't were lucky to find this class as I was. Thanks Prof. Freitas!

@nickiexu7259 6 жыл бұрын

This whole set of videos on machine learning is so well done and everything was explained in molecular details. Great teacher with exceptional teaching ability! I feel truly blessed.

@cakobe8 8 жыл бұрын

I truly appreciate these lectures. Thank you very much professor, great pacing, great structure, great content!

@crestz1 4 ай бұрын

beautifully linked the idea of maximising likelihood by illustrating the 'green line' @ 51:41

@jiongwang7645 5 жыл бұрын

God bless you, professor Freitas!

@saidalfaraby 10 жыл бұрын

I wish i watch this video earlier before the midterm.. Cool, your explanation is always amazing.. Thank you..

@havalsadiq3655 11 жыл бұрын

Very very clear explanation, I have spent a lot of time about learning probability, just now everything became clear. really very smart professor!

@yy8848 11 жыл бұрын

The lecture is great! It is really helpful. Thank you.

@AlqGo 7 жыл бұрын

Thank you. This lecture alone has consolidated many fragments of knowledge that I have about linear regression! It's like almost everything clicked for me. I do still have a big question. Why is the standard deviation also estimated by minimizing the log-likelihood? What makes it an appropriate estimate of the standard deviation of the same normal distribution that has the mean (x^T)*theta_ML?

@joeleepee 11 жыл бұрын

Smart professor!

@gruppenzwangimweb20 8 жыл бұрын

great intuition for MLE

@jhonathanpedroso7103 10 жыл бұрын

Great lesson!

@marcuswallenberg4492 9 жыл бұрын

Great stuff, although I wonder, should the normalisation constant for the multivariate normal pdf at 19:00 contain a factor (2*pi)^(-n/2) (since it's stated as a general multivariate Gaussian)? If it's still supposed to be the bivariate example, I missed that...

@jonpit4342 3 жыл бұрын

Exactly, as you pointed out it should have negative n over 2 since it talks about n random variables

@SNPolka56 8 жыл бұрын

Excellent lecture ....

@mrf145 9 жыл бұрын

Superb!

@DivakarHebbar 6 жыл бұрын

+1 for your sense of humor! :) Great lecture.

@KrishnaDN 8 жыл бұрын

Perfecto

@funfun_sci 4 жыл бұрын

awesome lecture

@ahme0307 11 жыл бұрын

at 1:12:38 is a bit confusing. I think it should be the information that the unfair coin toss reveals to us is less than one heads-or-tails. am I missing some thing?

@user-qh8zx7zo2u 5 жыл бұрын

i'm not sure about this, but the way I undertand entropy is as a measure of randomness, thus when you have a fair coin, you have the highest entropy since all events in state space are equally likely. If you have an unfair coin you gain more information about what the value will be next time coin is flipped. If you take limiting cases you have max info gain and min entropy since every throw will result in 0 or 1. In later lectures when he talks about decision trees and information gain he explains this.