Видео

The concept is indeed, as you wrote, thought of as a function that associates 0/1 to the data points: 0 for those point that do not belong to "the concept" and 1 for those that do. The same is achieved by considering the set of those data points whose label is 1. In this way, the concept is a set: the set of the points labeled 1.

I know, for this video I had a slightly different setup: different microphone and a different room (some echo can be heard). Tried to smooth it in the editing software, this is the best I could get.

@@MLClassroom I understand, noise is often difficult for me too.

I am sorry but I cannot provide the slides.

@@MLClassroom its ok sir. But your contents are really awesome! Thanks for the lectures

@@abrarhasan4197 Thank you, I am grateful for your feedback.

Thank you so much for the positive feedback! I Have one playlist that contains all videos in the order that I teach them in my university. And there are many playlist focusing on a single topic, with several videos building up to the mani aspects of that topic. I am not sure that one is more fundamental than another. They are all fundamental in the sense that they explore the mathematical foundations of the various topics, and they try to be lucid about the methods, the reasons they work as they are, and their limitations. I am in fact very curios how different people find the different topics and the way I teach them.

@@MLClassroom Ah yes I see the big playlist where it is in order thats what i was looking for!

There is a long and interesting discussion about 0: whether it is a natural number or not, and even whether it is a number at all. A brief hint at it is on Wikipedia (en.wikipedia.org/wiki/Natural_number). Historically, 0 is a late addition and conceptually/philosophically there is no agreement whether 0 (standing for "nothing") should be called a number, or whether something we use for mathematical convenience. Some more details on this are at mathfoundations.lti.cs.cmu.edu/class2/naturals.html, wiki.c2.com/?NaturalNumber, ruclips.net/video/6upQWEpHhuc/видео.html.

Thank you, great to hear this was useful!

Thank you for your kind comments, glad to have helped!

On k-NN I have a video at ruclips.net/video/glSGhHjnSFI/видео.html. On Parzen-Window I do not, but I do have several videos on kernel methods in this list. I hope this helps.

@@MLClassroom Thank you so much. If possible, please upload a video on parzen window as well.

Thanks for your positive comment!

Thank you for your kind comment.

Thanks for your message, great to hear you find them useful!

Mulțumesc pentru mesaj, sper sa va ajute videourile astea.

Thanks, good to know this was useful.

Thank you for the suggestion. For most videos I have the slides, for some (like this one that we comment on) I do not, as I wrote directly on the screen all the text. I am not sure yet on a good platform to share them on, and a good way to cross-reference them with the videos. RUclips is not quite right for this.

@@MLClassroom Thanks Prof, may be you can add your lecture slides to your personal portfolio where we can use them for studying!

@@NabidAlam360 what would be a good platform to host them on?

pi_k are the relative contributions of each individual Gaussian model to the overall mixture model. They are between 0 and 1 and they sum up to 1.

Glad it was helpful!

@@MLClassroom Professor, it would be very helpful if you manage to upload videos on attention and transformers!

@@NabidAlam360 thanks for the suggestion, nice and important topics. Not sure when I may get to them though. FYI: code companion for these lectures available at github.com/ionpetre/FoundML_course_assignments.

Welcome!

Thanks!

From the fact that P(R|C)+P(~R|C)=1.

Welcome!

Great to be able to help!

I'm so glad to hear, thanks for sharing.

Thanks! Just some random toddler shouts in the background (home studio 🙂).

Of course, here is the link to the entire series: ruclips.net/p/PLbkSohdmxoVAZ9DEHEWHjeGK7Ei-DjKHI&si=kZczY-WTZwqKAu_j

​ @MLClassroom Thanks for the reply! I saw a bunch of machine learning videos in the playlist, but only lectures 1, 6, 8, 10, 14, 18, 20, 22 on graphical models. Am I missing something?

Not more than this I am affraid @@Howard1130

@@MLClassroom Ah ok, thanks anyways! Great content!

Glad it was helpful!

Glad you liked it!

I'm so glad to hear!

Glad you enjoyed it!

Thank you, great to hear this was useful for you.

Hello, great to hear from you. I am very glad to hear that are so active, while still in high school. Way to go! I can of course recommend my own MLClassroom playlist (that this video I part of), a collection of more than 150 short lectures on various aspects of machine learning. You can access it here: ruclips.net/p/PLbkSohdmxoVAZ9DEHEWHjeGK7Ei-DjKHI&si=hwEWpZtCCfF_nQjU. The playlist is primarily meant for university students, especially in math, and it may not be the best starting point for you right now. Check out the many courses offered by coursera.org, especially those lectured by Andrew Ng. Even for those that require a fee to get access to, you can typically audit them (go through all their materials, including lectures and assignments, but without the peer review and the final certificate). You can also apply for a registration waiver, I am quite sure they will consider your application. Stay curious, keep learning!

You are welcome!

This video is based on "Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020." The full bibliography for the entire course is: Coursebook: Ethem Alpaydin. Introduction to machine learning, MIT Press, 2020. Other materials: Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, Second Edition, 2018. Avrim Blum, John Hopcroft, and Ravindran Kannan. Foundations of Data Science. Cambridge University Press, 2020.

Thank you, I think you have a good point here.

You're welcome!

The full playlist is available at ruclips.net/p/PLbkSohdmxoVAZ9DEHEWHjeGK7Ei-DjKHI. They are listed there in the order I teach them in class.

Thank you, glad to hear this has been helpful to you.

You are most welcome, keep up the good work!

Thank you very much!

Great to hear that I could help.

You are welcome!

You are welcome!