@solaaar3 and even still, many who paid get absolutely terrible professors. Finding someone this passionate about teaching is a matter of luck at this point
I don't know what to say about Professor Chris he's very energetic and amazing professor I ever seen in my life like him actually the hardest subject is probability he teaches just like that in simplest way thanks Stanford giving us opportunity to learn those who aren't Stanford students
Amazing professor... I am surprised though he gave a (kind of) Vann diagram explanation with the SARS problem only at the very end, and should've probably explained the confusion matrix rather than label it non-important
I think at 37:10 professor did not make it quite clear for probability = 0. The student confused probability with possibility. It is totally ok for thing A that is p(A) = 0 to happen to some extent. Am I right?
I’ve rewatched my college lecture 4 times and didn’t understand. I still didn’t really understand until...Netflix. The penny dropped. Lectures need to be started with these kind of examples. Thank you Stanford. Picking up for useless lecturers around the world
@greielts75331 Well, I guess its a good lecture as a first pass intro into Probability. A rigorous one can be the second pass. Do you have a link to a more rigorous lecture?
I hope the students realize how lucky they are to have this professor
not really lucky, they paid a fortune to get there..
@solaaar3 and even still, many who paid get absolutely terrible professors. Finding someone this passionate about teaching is a matter of luck at this point
This is a great lecture series! I love how he constantly keeps the big picture in mind, makes retaining this so much easier.
I don't know what to say about Professor Chris he's very energetic and amazing professor I ever seen in my life like him actually the hardest subject is probability he teaches just like that in simplest way thanks Stanford giving us opportunity to learn those who aren't Stanford students
This course is amazing..the professor is great. This is the only prob course I have taken which keeps me awake throughout the lecture.
the best probability professor ever
class starts at 2:55
Amazing professor... I am surprised though he gave a (kind of) Vann diagram explanation with the SARS problem only at the very end, and should've probably explained the confusion matrix rather than label it non-important
This guy is good.
what is the answer for the question given in the end of the lecture
I got this:
K = Knows the concepts
C = Correctly answered
P(K|C) = P(C|K) * P(K) / P(C)
P(K) = 0.75
P(C|Kc) = 0.25
P(Cc|K) = 0.10
P(Kc) = 1 - P(K) = 1 - 0.75 = 0.25
P(C|K) = 1 - P(C|Kc) = 1 - 0.25 = 0.75
(P(C|K) * P(K)) / (P(C|K) * P(K) + P(C|Kc) * P(Kc))
(0.75 * 0.75) / (0.75 * 0.75 + 0.25 * 0.25)
= 0.9
@@young_money Sorry, I think this: P(C|K) = 1 - P(C|Kc) = 1 - 0.25 = 0.75 is wrong!
p(C|K) = 1- p(Cc|K) holds but the above does not holds every time
K = know the correct answer
G = gets the answer correct
We know that:
P(K) = 3/4 = 0.75
P(G|Kc) = 1/4 = 0.25
P(Gc|K) = 1/10 = 0.1
So:
P(K|G) = P(G|K)*P(K)/P(G)
= P(G|K)*P(K)/(P(G|K)*P(K) + P(G|Kc)*P(Kc))
= (1 - P(Gc|K))*P(K)/((1 - P(Gc|K))*P(K) + P(G|Kc)*(1 - P(K)))
= (1 - 0.1)*0.75/((1 - 0.1)*0.75 + 0.25*(1 - 0.75))
≅ 0.9153
@@chamirus1 yep this is correct!
43:24 To the student's point, isn't it the case that P(E|T) = P(T|E) iff P(E) = P(T) and P(E and T) =/ 0?
I think at 37:10 professor did not make it quite clear for probability = 0. The student confused probability with possibility. It is totally ok for thing A that is p(A) = 0 to happen to some extent. Am I right?
ya, P(A)=0 means that the probability of event A is immeasurable. It does not necessarily mean that it is impossible.
I’ve rewatched my college lecture 4 times and didn’t understand. I still didn’t really understand until...Netflix. The penny dropped. Lectures need to be started with these kind of examples.
Thank you Stanford. Picking up for useless lecturers around the world
lovely!
To E or not to E? That is the question
Better out than in
Explaining bayes theorem with poop. Legend.
This guy is not rigorous.
@greielts75331 Well, I guess its a good lecture as a first pass intro into Probability. A rigorous one can be the second pass. Do you have a link to a more rigorous lecture?
What a poopy explanation 😅