Week 1 Lecture 2 - Supervised Learning

Supervised learning, classification and regression, examples, linear mapping, quadratic mapping, performance, over-fitting, inductive biases – language bias, search bias, training set, test set, validation set, applications of supervised learning, sum-of-square error minimization.

[Molaga]

Clear and comprehensive information. Sincerest thanks.

[lidiyapriyadarsinik]

Who can give a thumbs down to this lecture is beyond my understanding

[mukulrana]

Thank You Sir. Precise, Simple and onto Point

[jaivikkachhia]

11:19 correction in transcript

Y's are not numeric. instead of wise or not

[nikhiljerin]

If someone can’t understand these slides. Kindly learn algebra and statistics

[anjalinaudiyal]

good morning sir, please tell me how can i download your notes

[yugandharkanaparthi]

sir please share the notes regarding to machine learning course