Machine Learning Lecture 11 'Logistic Regression' -Cornell CS4780 SP17

[vatsan]

Am I the only one who raises his hands from my home whenever he says raise your hands? :P

[Enem_Verse]

So Many professor have knowledge
But only few have enthusiasm while teaching

[kodjigarpp]

You literally saved my comprehension of Statistical Learning, thanks!

[sandeepreddy]

Awesome lectures !! glad to have bumped into one of them; after that spending time on the entire series felt worthwhile.

[smallstone]

A fantastic lecture. Thank you professor.

[rolandheinze]

Thanks for posting all these lectures Dr. Weinberger. Should make Siraj Rival aware of their availability!

[vatsan]

I love the way he gets so excited when he says TADA! xD

[dhrumilshah]

This video lectures are great ! completed 12 in 2 days ! I find it more intuitive than Andrew Ng one . Also Prof have you ever recorded lectures on unsupervised learning ? would love to watch those since those are missing from this series.

[YulinZhang]

This is great stuff. It's just funny that those are motorized chalkboard instead of dry erase boards.

[ugurkap]

It could also be nice to see a dataset correctly classified by Naive Bayes and that if Logistic Regression optimizes the hyperplane even further.

[mhsnk]

@KilianWeinberger Unfortunately online viewers don't have access to the course homework but I think your claim in 20:31 is only valid if across each dimension, data from classes +1 and -1 happen to come from the same variance Gaussian distributions. Otherwise, you would need quadratic terms too.

[xiaoweidu]

Weinberger is one of the best machine learning lecturer

[taketaxisky]

Interesting to learn the link between naive Bayes and logistic regression. Thank you! For the spam email example with very high dimension feature, logistic regression won’t work right.

[nrupatunga]

Hi Kilian, The flow of your lectures are awesome. How you build upon the concepts is amazing.
Do you have Matlab codes shared publically? Really cool demos

[jachawkvr]

It was nice learning about the connection between naive bayes and logistic regression. However, at the moment, I am only able to see the connection between GaussianNB and Logistic regression. Is there some way to logistic regression if the features are not real-valued?

[sudhanshuvashisht]

I couldn't prove that Naive Bayes for continuous variable is a linear classifier except for the case where I assumed the variance doesn't vary across labels (spam, ham as an example) of y and only varies across input variables x_alpha. Was anyone able to prove it?

[sinhavaibhav]

Since we are using the same form of distribution for P(Y|X) for NB and Logistic Regression, are we still making the same underlying assumption of conditional independence of Xi|Y in case of Logistic Regression. Or does directly estimating the parameters of P(Y|X) means that we are relaxing that assumption?

[sekfook]

can we say that Gaussian Naive Bayes is logistic regression In the case of continuous features?

[satyagv]

Hi Kilian, As Naive Bayes comes up with hyperplane that separates two distributions rather two datasets, can the same statement hold good even if the input data set is highly imbalanced..? I mean with out balancing can we still proceed..?

[JoaoVitorBRgomes]

So what's best to find the P of logistic regression? MAP or MLE?