Do you mean beyond Bayesian Regression and Inference? … I am trying to base most of the tutorials in this series using examples and plots from PRML provided it makes sense. For example, the latest (Markov Chains) in PRML is not covered to my satisfaction.
@@KapilSachdeva So here is how I see it, A lot of Advanced ML courses abroad require you to have prerequisites in the form of PRML or the MML book. At least if someone wants to understand research papers a lot of them assume students know chapters from PRML like Kernel Methods and Approximate Inference (which become important in Generative models, VAEs). Your videos are super nice so if you are working on a series on these lines I believe undergraduates can start understanding the subject in more depth and also start contributing and try their hand in research. Definitely, if you feel find other books to make more sense for a particular topic that is the way forward, but its just the confidence it gives students knowing they covered the prerequisite book with the reputation of PRML feels good.
All fair points. Thanks, your suggestion makes lot of sense and see the value in it. Let me ponder over it on how to execute on this great suggestion ! 🙏
This was a wonderful tutorial. Your graphics, style, clarity and humour are very helpful. Looking forward to the rest of the series. Specifically, I'm interested in using Bayesian prediction on my own data.
Really a treasure of knowledge expressed in very simple way….a help to learn Bayesian treatment. Please complete the series and more videos are expected.
Thank you. Very helpful! But I think there is a mistake in saying the weights vector is a row vector. I believe it should be a column vector for t = Xw
Amazing lectures, thank you so much! I was wondering at 12:07 the partial derivatives of E wrt w_i (in the right panel), shouldn't those be the partial derivatives of ys and not Es?
How to create this type of video. Just curious its spot on with black background and animation is enough to serve the purpose. Any lead is appreciated 👍. Thanks for the video and the content.
This is an amazing series, please continue doing videos for rest of "pattern recognition and machine learning" book
Will do.
Feeling guilty now :( …. Apologies for the delay.
Yeah..this is really helpful...I was struggling to get sense of these equations but then this video has very systematically explained the concepts!!!
@@KaranYadav-hw9yo thanks 🙏… glad you found it helpful!
Please do more from PRML! A whole generation of us need it :) Thank you!
Do you mean beyond Bayesian Regression and Inference? … I am trying to base most of the tutorials in this series using examples and plots from PRML provided it makes sense. For example, the latest (Markov Chains) in PRML is not covered to my satisfaction.
@@KapilSachdeva So here is how I see it, A lot of Advanced ML courses abroad require you to have prerequisites in the form of PRML or the MML book. At least if someone wants to understand research papers a lot of them assume students know chapters from PRML like Kernel Methods and Approximate Inference (which become important in Generative models, VAEs). Your videos are super nice so if you are working on a series on these lines I believe undergraduates can start understanding the subject in more depth and also start contributing and try their hand in research. Definitely, if you feel find other books to make more sense for a particular topic that is the way forward, but its just the confidence it gives students knowing they covered the prerequisite book with the reputation of PRML feels good.
All fair points. Thanks, your suggestion makes lot of sense and see the value in it. Let me ponder over it on how to execute on this great suggestion ! 🙏
@@KapilSachdeva Thank you! Really looking forward!
🙏
This channel is gold mine seriously. Thank you Sir!
🙏
I just found your channel today and I am glad I did! Great stuff
This was a wonderful tutorial. Your graphics, style, clarity and humour are very helpful. Looking forward to the rest of the series. Specifically, I'm interested in using Bayesian prediction on my own data.
🙏
Really a treasure of knowledge expressed in very simple way….a help to learn Bayesian treatment.
Please complete the series and more videos are expected.
🙏
such a gem of the playlist. Every topics are explained clearly. Please do more videos with the same book❤❤❤
🙏
This video is so helpful for me after I finished my regression class. I didn't under the overfitting concept. Now I do! Thank you!
🙏
Very well explained. Please keep up the good work!
🙏
This is amazing, I hope you make more. Thank you.
🙏… there are 10 more videos in this playlist called “Towards Bayesian Regression”
Thank you. Very helpful! But I think there is a mistake in saying the weights vector is a row vector. I believe it should be a column vector for t = Xw
🙏 thanks for pointing it out. You are correct. I will add a note in the description.
Very helpful videos, thank you very much!
🙏
That is super useful! Fantastic work :)
🙏
Great video, thanks !
🙏
Thanks for excellent lecture
🙏
Amazing lectures, thank you so much! I was wondering at 12:07 the partial derivatives of E wrt w_i (in the right panel), shouldn't those be the partial derivatives of ys and not Es?
No it is E, the error function. Our goal is to minimize the error.
How to create this type of video. Just curious its spot on with black background and animation is enough to serve the purpose. Any lead is appreciated 👍. Thanks for the video and the content.
🙏 PowerPoint with black theme and morph transitions and animations. Not much more than that.
@@hyperadapted sometimes in latex but mostly in PowerPoint :)