is there any lecture notes in PDF format? Thank you

Nice class sir

Thanks sir for this information

개추

From Bangladesh University of Textiles.. These Series is just excellent.. Loved the contents.. Best wishes...

The book is very expensive, and i dont know how nptel allowed an entire course based on marketing of a book.

If you know the trick it's not magical anymore

Very well explained.

Can you write a book and name it Mausam Decision Process or Krushna Decision Process. Hum kyun Markov naam jaap karein?

Thanks a lot but some typos are there

Is the expected value or Utility of outcomes positive for the courses of IIT Delhi designed by Ambachcha?

Topics Covered: 1. Properties of a Good Estimator 2. Unbiasedness 3. Consistency A sufficiently good lecture. Thanks sir.

Topics Covered: 1. Distribution of r^th order statistic 2. joint distribution of r^th & s^th order statistic

Quite helpful.

If we can get English subtitles

Thanks sir.

fantastic ..... woooooo , the saviour , love from MNITJ

Does teaching AI benefit an average Bharat, asking in a non combative way.

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Learning - When Bharat is a poor country and money can be earnt through co operation, we can minimize uncertainty by leaving interpretation aside where AI has a lead of using past associations.

Is this course beneficial for mechanical engineering students?

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A good revision.

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Nice video sir

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Do you proof for me some exercise ? I am very thankful if you help me. Problem 1 : Let X and Y be two random variables such that X only takes values a 1 ,..., a_n and Y only takes values b_{1} ,...,b_n , Prove that X and Y are independent if and only if mathbb P ( X =a t 1 Y=b y )= mathbb P (X=a i ) mathbb P (Y=b j ) for all 1 <= i <= m 1 <= j <= n Problem 2. Let X, Y, Z, T be independent random variables. Prove that X + Y is independent of ZT. Problem 3. Let X, Y, Z be independent random variables. Can we conclude that X - Y is independent of YZ? Why or why not? Problem 4. Let X and Y be two independent random variables defined on a probability space (Ω, F,P), and let f, g / \mathbb{R} -> \mathbb{R} be measurable functions. a) Prove that f(X) and g(Y) are random variables defined on the probability space (11,F,P). b) Prove that if f(X) and g(Y) are integrable, then so is f(X) * g(Y) and \mathbb{E}*f(X) * y(Y) = \mathbb{E}*f(X) * \mathbb{E}*g(Y)

I need help with my career… I am interested in Artificial Intelligence… so which subject should I take in 12th class?

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Great brief tutorial.

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In case of value prediction. at which stage we are validating correctness of predicted value.

Good

Machooooo

THANK YOU SIR 🙏

This Indian people are the goats, thanks.

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I don't understand how the receptor can estimate two channel with just one signal

Better you explain in hindi