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This was great because i needed to understand this for my exam tomorrow.

volume is too much low

thank you so much.

Thank you, could be please share the homework link here if it's possible?

Thank you

Fantastic video explanation! Crisp, clear and formula-based. Easy to follow once you know the concepts and this video helps us clear the confusion among these fancy terms like joint, conditional and independence.

The audio is not clear. Very bad audio.

26:43 Isn't it Smoke is conditionally independent of Alarm given Fire?

20:43 His explanation is quite confusing.

The audio is not so clear.

Haha

Good explanation of likelihood weighting, very helpful

The content here flows extremely well. Thank you for making it public.

start at 9:22 if you know probability, if you don't this is a terrible introduction and I'd suggest watching the 3b1b videos on bayes rule. a good textbook is intro to probability by blitzstein hwang

It is just an application of bunch of expressions without a context and a delivery of logic. In my opinion, it does not teach the one anything, but just gives things to memorize.

Is there no need to normalize?

Best Explanation

Perfect!

👍

Thanks for your very clear explanation. For more examples on d-separation see this th-cam.com/video/yDs_q6jKHb0/w-d-xo.html

sound volume is too small.

Is this really MLE? Or is it MAP? 'XD

Perfect!

Really appreciate it.

Shouldn't the score of Alpha A1 at @11:00 be decreased and alpha B1 be increased since B is the right class?

Hi, at 4:17 didn't you do a switcheroo of the formula? Like instead of P(x,y) = P(x)P(y|x), it should've been P(x,y) = P(y)P(x,y) ? From what I hear in the video, this is the way you explained.

The content here was extremely helpful, but the audio was really poor. Still, I wouldn't have figured this out without this particular video, so thank you!

*for first question 2(-1) + -2(2) = -6 not -2

By far the most efficient source of information about this topic I could find anywhere on the internet

This was great!

Hey sorry but I don't get why we sample whereas we already have the true distribution... I don't see how it can be useful... Does anyone have an explaination please :)

Great explanation!!! Thank you!

Dude I love you all for posting these lectures but this is a 75 mins one on how to multiply two numbers together. Soooo painful :) <3

Really got a lot out of this thank you!

How do you calculate the update weight? Could you provide an example to calculate it?

Unfortunately the explanation of calculating entropy and information gain is very unintuitive.

concerning ex. 2f: isn't the largest factor generated 2^4? because the join on all factors containing T generates a table over 4 variables (say f2') of which one is summed out to get f2. so f2' has size 2^4

Thanks for video. I am just wondering how we normalize to sum to 1 in part g. Can you give any numerical example? Thanks

Can anybody please explain results on the slide at 1:05:11 for the given probability tables?

Audio not goooooooood

1:17:14 It is bad example for LCV !!! This case never happens because MRV heuristic will color SA with blue (one color left !!!)

This is very helpful! Thank you