*My takeaways:* 1. DIstance 9:30 2. k-means algorithm 17:03 - How to choose k 23:57 - Unlucky initial centroids 25:56 - An example 28:58 - Scale data into the same range 37:11

Professor gifting the ones who contribute to this lecture. Loved that👏💝

The explanation level of this video is by far the best I have ever watched. Prof. Guttag does a very good job in explaining every concept more clearly.

thank you mit! from singapore . lots of love

Pure gold... thank you so much.

Great lecture ! I attended the Clustering lecture by prof Ayan Seal today (even though I dont have the course : Introduction to Data Science) , he didn't focus a lot on code, but had similar things to share about clustering !

Very good class! Thanks for the video and for the knowledge!

it just struck me, after all these lecture videos, that professor Guttag is actually using a classic positive reinforcement technique to make the students more attentive and responsive in class by giving out candies for correct answer. lol! and i am not sure if its the result of this or something else but the students seem wayyy too eager to answer questions in this paticular lecture video!

Thank You MIT.

great professor

love that prof for 4:35 - that is brilliant

K-Means at16.30: one of the very best algorithms in IA

This professor is awesome!

I wish I had this professor, would probably love algorithms

I wish I get the opportunity to sit in a class at MIT someday! Such brilliant minds

Awesome explanation.

I cant believe I am binge watching MIT lectures. I wish I had a chance to attend MIT back then.

thank you Prof!

Great lecture! Informative AND entertaining.

great lecture! at the speed where it is easy to understand

Always helpful, thanks 🧮

Amazing!

I wanna attend Professor Guttag's classes mostly for the education but also for the candies.

That's one amazing lecture !

Thanks MIT

Thanks for an amazing lecture! @29:35 it tries to cluster data into two groups and see if it correctly differentiated people who dies of heart attack and those that didn't. To me this is using clustering for classification task, if yes, when would someone use clustering rather than classification?

Thanks for the lesson professor, it's really good explanation

I guess that the statment that he was trying to set as True to scale the data was at line 14. Awesome lecture! Thanks.

39:54 I think z-scaling is the same as creating a normally distributed dataset

it's dendRogram, with an R. Comes from the word for "tree"

When we are clustering the airports, the professor only stopped to think about linkage when he arrived at Denver. Shouldn't we have thought about it since the beginning of the clustering? If so, we could have gotten (BOS, SF) instead of (BOS, NY) for the first iteration using complete linkage.

To quote Dr. Banner: ‘Basic cluster recognition’...

The guys who down voted this video must had nothing better to do. The lecture was nicely paced and I think he already made the problem as clear as it can get. Anyway, that was a great lecture. A big thank you to Professor Guttag and the MIT OpenCourseWare team.

Main issues of K-Means : choosing the number of clusters (k) and data scaling: But what if one wants to apply weights to the features (parameters)? Should you just multiply the features with the desired coefficients?

i like how Dr. Guttag just throws candy at the students

awesome class. I craved candy while watching it

29:06 Isn’t the heart attack example a case of supervised learning, since we have the labels? 1:59 At the start of the lecture, the professor mentioned clustering as an example of unsupervised learning

What are some some methods to evaluate the quality of the clusters, if we do not have an outcome variable? In the example they were evaluated based in part based on whether the subjects in the cluster died at a higher rate. What do I do if I don't have an outcome to look at, only characteristics? For context, I'm creating cognitive style groups based on user data for an insurance company, and these styles will be later used for morphing, churn etc. but do not have an outcome variable per se.

Professor Guttag: 'Dendrogram... I should write that down.' also Professor Guttag: mispells it :D

Why we use clustering while we have the label? Like in the medical example, we already know the label (0,1).

I am feeling stress like in a class with a bunch of genius.

at 46:50 the professor mentions “has pretty good specificity, or positive predictive value, but its sensitivity is lousy.” can someone explain how specificity = ppv? im assuming: ppv = tp/(tp+fp) specificity = tn/(tn+fp) doesnt ppv = precision?

Each one choose for itself...

16:10 could anyone explain what the professor is talking about when he's mentioning n-squared and n-cubed algorithms ?

Hello, I come from wet lab and I am not familiar with machine learning. But I am really interested in this topic since I want to apply machine learning to my research in plant genetics. I have watched this video several times but still I have not gotten all the things the professor mentioned. I wonder if the author or anyone can share the lecuter or books in this topic. It will mean alot to me. Thank you in advance.

"Clustering" is usually taught to "signal" "alumni" that anyone "in their *network*" can't learn and be good at some skills because some Terrorists in their "*network*" may be affected andor effected.

Ok, by minute 7 my mind is wondering if there's going to be a bonus assignment to find the probability that Professor Guttag will correctly throw you the piece of candy on the first try. The odds of you catching it greatly increase the closer your sit to the front center of the room.

This is way more comfortable when at 1.25 speed.

Thats so fucking cool. Explaining how to group data and throwing candy at your students for answering right

40:00 why is mean 0 and standard deviation 1?

At 28:00, can anyone help here ? How do we compare this dissimilarity (mentioned in IF statement), in Python. Badly need this.

Data scientists actually have to think. Good one

What was the thing that John Guttag threw at the student

Is the full code of his examples accessible?

MIT: 2 kinds of people. Harvard: ......... Princeton: .........

what is the average of examples in the same cluster?

What does he throw to the students who answers ?

how do we test different k values when examples are unlabeled?

From where can I get the pdf of the same. OR some notes.

I want that choco

Can anyone link machine learning to digital signal processing for me?

What’s the name of the course? And in what college ?

Does someone know the name of the book 📚 used and where to access the code he mentioned he distributed?

23:20

Prof john Guttag has banch of Candie's

so we are getting candies for every right answer, i am 26 years old and heck yeah!! i would still love to have free candies 👍😜

14:20 the distance from Denver to Seattle is 1307 and the distance from Denver to Boston is 1949, so why he clustered Denver to Seattle instead of Boston when using Complete linkage? should it not be clustered to the greatest distance?

what is the reference book ?

great lecture but the cholate did not reach me.

He is treating you like pets. Like little hamsters.

This man has the mannerisms of Bill Gates

Normal Playback = 1.5x speed

1:45 Democrat/Republican... Smart/Dumb... Professor, you're being redundant!

Take care, students, with democrat teachers in computer science classes. They don't care to play with you and call you a dumb if you are a republican and later ask you to choose who is the dumb and who is the smart. I hope you grades doesn't be influencied by you political bias.