Introduction to Machine Learning - 11 - Manifold learning and t-SNE

Lecture 11 in the Introduction to Machine Learning (aka Machine Learning I) course by Dmitry Kobak, Winter Term 2020/21 at the University of Tübingen.

[@justuslau2894]

Absolutely amazing video course. Especially after looking at other sources I notice how valuable this is. Every video achieves to combine the intuition and math in a concise was. I recommend the videos to anyone who wants to learn about ML.

[@graedy2]

The best video on this topic I have found so far by a large margin. Excellent work!

[@oncedidactic]

Excellent talk with spot on visuals and explanations. Thanks!

[@Denverse]

Amazingly explained, It's such a great resource.

[@benjaminbenjamin8834]

what an amazing explanations.......................well done............BRAVO!

[@Anthonyj74816]

It is an amazing course, worth the time to watch and learn from it.

[@woodworkingaspirations1720]

Worth every second. You are a blessing to humanity.

[@wenkangqi9877]

Amazing Lecture, very well explained! Thank you for sharing!

[@migueldelvalle8975]

Incredibly explained. Congratulations!

[@tusharv204]

Beautiful explanations!

[@brianchaplin278]

Great explanation with both details and good examples

[@artemshcherbakov7550]

Amazing! Super interesting and understandable!

[@berkoec]

So well explained! The best video resource I have seen on t-SNE so far!

[@peterhemmings2929]

Top quality lecture, thanks for sharing

[@brianlarocca4390]

Wonderful job. Really enjoy watching this.

[@user-vm9hl3gl5h]

23:20 perplexity - adjust sigma for each i so that we reach perplexity=30. may be small in dense group, but big in sparse group. 43:40 crowding problem

[@Bwaaz]

Amazing course with great vizualisations ! thank you very much

[@vivaliberte]

Awesome explanations. Thank you very much.

[@robertzell8670]

Fabulous video! This was really helpful, thank you!

[@cihanulas5438]

Thanks a lot for greay content

[@warrenarnold]

Thank you for your awesome explanation and illustrations nive thank you very much

[@StarHeartsong]

Bravo! Thank you very much.

[@joswarbellidorosas3956]

Thank you for this course!

[@aashishmalhotra]

amazing content

[@MrDesperadus]

Excellent lecture, thanks

[@juanete69]

Great lesson. How can you use t-SNE not just for visualization but also for classification? Does t-SNE take into account that some variables are more related with the formation of the cluster and other just add noise? I mean, in some moedls you can calculate the p-value and the SHAP for each variable. Can you get this kind of information here?

[@DucLe-kg5hx]

amazing lecture. Please post more videos.

[@jiaxuanchen8652]

Amazing!

[@taraxmetodopilates3658]

Greetings from Spain

[@manfungyu9853]

Very good lesson

[@automatescellulaires8543]

amazing, thx.

[@dragolov]

Respect!

[@aricanto1764]

This video is the bees knees

[@DM-py7pj]

how can one get good results with PCA init as don't we lose valuable non-linear information?

[@willw4096]

t-SNE is 1) non-linear 2) non-parametric (aka stochastic, non-deterministic) 3:28-4:20 8:46 MNIST 9:22 PCA's visual 17:14 17:57 18:45 t-SNE's visual 31:29❗2 separate blue clusters cannot get together 32:41 the fix: increase "Early Exaggeration" temporarily to increase the attraction force and then decrease back

[@ccuuttww]

It's like your cup when u add the coffee powder into water

[@1potdish271]

Where can we find lecture notes?

[@tiktokdok5233]

Use the subtitles/closed captions?