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Great lecture on data privacy and security! The content was very informative. Could you share some practical steps individuals can take to enhance their data security in everyday life?

Good lecture. I didn't understand the bit about how/why to incorporate entropy in the selection process, and how that would stop us from ending up with redundant examples, though -- anyone assist?

Awesome, thank you for sharing! One question though: IIUC all of this assumes that the model we plan on training is in fact a good estimator of the phenomenom we're trying to model. I understand how the algorithm works in that case. However, how do we validate that assumption? What if I'm using a terrible model, how would I know? After using confident learning to clean the datased I'd thing that I now have a better dataset, but I don't think that's achievable through a bad model.

An awesome course that doesn't have any substitutes online. Thank you so much for posting.

this course hard to learn, is there anyone just recommend any course should I take and then study this !!

paint > chalk

amazing lecture. thanks a lot

time 32:23 outliers! - look up Platypus ;-) egg laying mammal, D'oh!

What a good course, Thanks so much for sharing.

Thanks a million for making all course materials available, very wonderful course!

Thank you for sharing this course, it's fantastic!

Question: So far these lecture series is focusing on image recognition examples. Is DCAI also applicable to other data for example time series data for a predictive maintenance problem? If so, are there any fundamentals that change that need to be taken into account? Many thanks for putting these lecture online!

thanks for making this available. quality data is so fundamental for any reasoning... human or AI

thank you all!

Excellent course. Thanks a million for sharing.

5:02 Pretty funny that he misclassifies the camel spider again as he's looking at it (it's not a tick, but its not a scorpion either) - just shows how often these kind of errors happen.

Keep the good ideas going!! Employ a company like smzeus!

The formula for the t_j’s is not giving me the same values in the presentation and I have a feeling that I’m probably not applying it correctly. Can anyone explain how to get t_dog = 0.7 based on the given noisy labels and the predicted probabilities, for instance?

As the course comes to a close, I would like to take a moment to express my sincerest gratitude for your guidance and support throughout the lectures. It took me about a month to complete all nine lectures, including labs and notes, but I can say that this path has been the most illuminating experience in my educational life. Your unwavering dedication to teaching and commitment to my learning experience has not gone unnoticed. You have inspired me to continue learning and growing beyond the classroom. Your generosity with your time and knowledge has made a significant impact on my journey. Thank you once again for all that you have done for us.

Very informative and concise lecture! Good didactic, involving students (who seem to have good knowledge of the subject matter). Congratulations from 🇲🇽

Thanks for the amazing lecture!

Could anyone told me why the slide in 37:43 align the left most image which has noisy label:dog and higest probability 0.7 as fox to y~ = fox and y* = dog in the table?

Thank you for sharing

This course covers unsupervised learning?

Thanks for posting this! Especially a link to the lab and notes :)

Thank you so much for this !!

Thanks for posting!