Machine Learning 3.2 - Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)

[@gingerderidder8665]

This beats my MIT lecture. WIll be coming back for more!

[@Spiegeldondi]

A very good and concise explanation, even starting with the explanation of likelihood. Very well done!

[@pinkAisle]

i was trying to read it my self but you made it so much simpler

[@billbasener8784]

Thanks! I am glad it was helpful.

[@lizzy1138]

Thanks for this! I needed to clarify these methods in particular, was reading about them in ISLR

[@JappieYow]

Interesting and clear explanation! Thank you very much, this will help me in writing my thesis!

[@billbasener8784]

How did your thesis go?

[@sunny_blue_skies]

I enjoyed watching your video, thank you. I will watch more of your videos on machine learning videos thank you!

[@huilinchang8027]

Awesome lecture, thank you professor!

[@spencerantoniomarlen-starr3069]

10:48 ohhhhh, I was just going back and forth between the sections on LDA and QDA in three different textbooks (An Introduction to Statistical Learning, Applied Predictive Analytics, and Elements of Statistical Learning) for well over an hour and that multivariate normal pdf was really throwing me off big time. Mostly because of the capital sigma to the negative 1st power term, I didn't realize it was literally a capital sigma, I kept thinking it was a summation of something!

[@filevich]

thank you thank you thank you thank you Bill !

[@Sam1998Here]

Thank you for your explanation. I also think at 8:15 the multivariate normal distribution's probability density function should have $\sqrt{|\Sigma|}$ in the denominator (rather than $|\Sigma|$ as you have currently) and it also may be helpful to viewers to let them know that $p$ represents the dimension of the space we are considering

[@Dhdhhhjjjssuxhe]

Good job. It is very easy to follow and understand

[@vi5hnupradeep]

Thankyou so much ! Cleared a lot of my doubts

[@geo123473]

Very great video! Thank you professor!! :)

[@黃楷翔-h8j]

Very useful information, thanks you professor!

[@billbasener8784]

I am glad its helpful! Thanks for the kind words.

[@jaafarelouakhchachi6170]

can you share these slides in the videos with me?

[@zhengcao6529]

You are so great. Keep up please.

[@saunokchakrabarty8384]

How do you get the values of 0.15 and 0.02? I'm getting different values.

[@rmharp]

Agreed. I got approximately 0.18 and 0.003, respectively.

[@kaym2332]

Hi! If the classes are assumed to be normally distributed, does that subsume that the features making up an observations are normally distributed as well?

[@billbasener8784]

Yes. If the each class has a multivariate normal distribution than each individual feature variable ihas a single variable normal distribution.

[@MrRynRules]

Thank you sir, well explained.

[@billbasener8784]

Thanks!

[@pol4624]

very good video, thank you professor

[@billbasener8784]

I am glad it is helpful. Thank you for the kind words!

[@mikeolinblare913]

948 Benny Glen

[@haitaoxu3468]

could you share the slide？

[@CottonChristian-e3r]

Young Carol Harris Ruth Clark Jessica