Probably Approximately Correct (PAC)Learning ( KTU CS467 Machine Learning Module 2)

[@akshayanilkawale5970]

There is big difference in reading and teaching

[@sergiotorrijos5911]

After some time I think I've understood it thanks to this video. Very clear explanation!!

[@ercanatam6344]

There are some notational confusions: at 4:32, you use Error (h), then write this P(CXORh) and then use "Pr". What is difference between Error (h) and P(CXORh)? For probability you use both "P" and "Pr", that is not consistent.

[@nishantkamboj6543]

Never saw a great explanation than this ❤️

[@tianshizhang458]

Detailed explanation and good PPT. I like it!

[@haji3787]

Great explanation. Thanks!

[@harsh4749]

14:40 where does inequality eq came from

[@sanITtips]

By applying Hoeffinding Inequality

[@mottymathews5832]

Great explanation. Thank you

[@yahyaemara6617]

Great video!, Thanks for explanation

[@nitya6828]

Can I get ppt of this video?

[@ramapathivada]

Asalu aaa error values ela vesaru?epsilon =0.05 endhuku

[@aldenmenzalgy3313]

Amazing!!!

[@kurmangee58]

Beautifully explained. Thank you!

[@saketnarendra8864]

Thank You maam, really well explained

[@9860215497]

Why epsilon and delta is upper bounded by 1/2?

[@rajkane989]

1/2 is just a conveniently small value that can be made arbitrarily small. Having error over 50% or probability less than 50% would make for a useless model

[@kalpanan343]

Very nice

[@abdokabany4807]

Thank you very much that was really helpful

[@ALEXPJOSEPHBTechCSEA-]

thanks

[@AryanSinha-s7p]

real use of ai and ml

[@akhilkumar220k.s9]

Ma'am could please share us the notes if possible.

[@fatimaishtiaq8422]

thank you. but can u give me its coding?

[@vishaltanawade7637]

We can read ,,,,