Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) 

SVM starts from 46:20

32:07 It helps me to think of Laplace smoothing as Pr(observation gets label) = (count of observations with label)/(number of observations) --> Pr(observation gets label) = (count of observations with label + 1)/(number of observations + number of possible labels)

1:11:20 I don't understand how w/17, b/17 can prevent increasing functional margin by just increasing weight and bias?

26:30 memo. he explains the difference between the multinomial event and the multivariate Bernoulli event model.

too many side quests in this level

Done!

just had a doubt....... at 54:56 , what does g(z) denote ? is it the sigmoid function ?

In 19:15 wouldn't it be more accurate to say multinouli instead of multinomial, since the concept of number of trials that's a parameter of the multinomial distribution doesn't really apply here?

Thanks for the great video! One question: 8:00, if you have this NIPS in your feature, were you even able to train your model if you don't have any email contains NIPS? Your MLE formula will yield 0 probability. (Or actually you not really train your model, you got analytic solution directly, and prediction just use the counting solution?) Thanks in advance for any advice!

where can I find class notes for this lecture? please any one know this

1/4 done!😵

A doubt : When talking about NIPS conference making zero probability in Naive Bayes ; in the first place, probability of word NIPS shouldn't come up in the calculation P(x /y=0) , as the the binary column vector of 10000 elements won't have this word in it as its not in the top 10000 words cuz it started appearing very recently.

laplace smoothy

at 35:21 shouldnt there be ni in general instead of the 10000 that is being added

Don't buy drugs, guys.

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camera person - please do not move it frequently next time. It should focus on what is written on board. You are tracing professor and losing content. We can relate voice to what is written on board. It should always be on vision what he talking. Your and his hard work got wasted a bit.

He needs to learn how to speak loud and more clearly... Otherwise it's a good lecture 👍🏾