Introduction to Machine Learning - 06 - Linear discriminant analysis

Lecture 6 in the Introduction to Machine Learning (aka Machine Learning I) course by Dmitry Kobak, Winter Term 2020/21 at the University of Tübingen.

[@saketdeshmukh6881]

I wish I had found this before my masters. intuitive with right amount of mathematical rigor.

[@YuchengLin]

So wonderfully presented! Whenever I started to feel there was much math, some cute drawings appeared to give me simple and visceral intuition.

[@micahdelaurentis6551]

These have been excellent videos so far

[@IamMoreno]

simply beautifully explained, sir you have all my gratitude

[@TheCrmagic]

Sir, You are a great teacher.

[@xiaochelsey880]

Great video. Thank you so much for showing all the math!

[@jiajieli5138]

Highly recommended Machine Learning Instruction!

[@woodworkingaspirations1720]

This solved my problem. Thank you sir. Needed a summarized view of the math. Perfect.

[@vincentole]

Great videos! Thank you for this.

[@calcifer7776]

this is gold, thank you

[@AD-ox4ng]

This is my guess for the number of parameters (in the covariance matrix alone) at 38:16: Full - p^2 (There are p*p distinct elements) Diagonal - p (There are only p distinct elements along diagonal, all else is 0) Spehrical - 1 (Same as diagonal but equal variance in all dimensions, so only one number to compute) If the model is separate, multiply the number above by 2, otherwise 1. Add 2p to account for the mean vectors as well. (There are p distinct means to calculate for each of the two classes)

[@CootiePruitt]

👍 Great video - thank you!

[@severian6879]

Excellent explaination! Thank u very much!

[@nauraizsubhan01]

Sir can you please tell Does this course offers any course related to robotics and autonomous systems, during the program.

[@sunshinebabe6203]

Thank you! :)

[@Jeremy-zs3nn]

Thanks for posting - very helpful video. I did get a bit confused with some of the notation. Looking at the slide titled estimating gaussian parameters (25:49) - the covariance matrix we're estimating is indexing over Ck which is the subset of the design matrix for which Y=k? are X and mu_k both matrixes or is mu_k a vector?

Thanks. Let me see... x_i is a vector (sample number i). mu_k is a vector (average over all samples belonging to class k, so with Y=k). Sigma_k is a matrix (covariance matrix over all samples belonging to class k). I usually use lowercase bold for vectors and uppercase bold for matrices.

[@Jeremy-zs3nn]

@ great, thank you for the quick reply!

[@indigod3323]

Very great teacher, I wish I could study in Tubingen

[@hfz.arslan]

Sir can you please share the slides or notes thanks