Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
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Published 2020-04-17
Andrew Ng
Adjunct Professor of Computer Science
www.andrewng.org/
To follow along with the course schedule and syllabus, visit:
cs229.stanford.edu/syllabus-autumn2018.html
All Comments (21)
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SVM starts from 46:20
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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)
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26:30 memo. he explains the difference between the multinomial event and the multivariate Bernoulli event model.
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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?
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1:11:20 I don't understand how w/17, b/17 can prevent increasing functional margin by just increasing weight and bias?
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Done!
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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!
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1/4 done!😵
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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.
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just had a doubt....... at 54:56 , what does g(z) denote ? is it the sigmoid function ?
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too many side quests in this level
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Don't buy drugs, guys.
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at 35:21 shouldnt there be ni in general instead of the 10000 that is being added
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laplace smoothy
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They lost 😭
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G a gente se vê se fala ET r viu o cafezinho tava no forno
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T amo demais essa noite foi tão rápido mas se Deus quiser vir buscar f xi tô falando w se quiser ir comigo te amo e fica tranquilo então obrigada pelo convite lá pegar o valor é é é só o mesmo do trabalho e depois do jogo e do trabalho é melhor hoje
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C BB GG GG GG GG e GG e GG GG GG GG e o e um pouco então né eu tenho e um beijo e o cafezinho e o carro de manhã r ER r viu o jogo é só no é só no é o nome de quem é
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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.
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He needs to learn how to speak loud and more clearly... Otherwise it's a good lecture 👍🏾
