@thefuckingpearl 2 жыл бұрын
Omg this was explained so clearly! We need more professors like him , that don't just jump right into the math but take time to make students understand concepts intuitively and then logically.
@CuongHut 3 жыл бұрын
"Throwing away information is the only way to fit the complexity of the world into our brains " - Sanjoy Mahajan
@wentaofeng5548 7 жыл бұрын
i really respect my professor, but man the guy in video is 100 times better in every aspect.....
@benmackenzie 5 жыл бұрын
100%
@adiosm57 2 жыл бұрын
@@benmackenzie NOPE, figuratively 10000%... if his respect as real is 100%...
@ledescendantdeuler6927 Жыл бұрын
"Throwing away information is the only way to fit the complexity of the world into our brain. The art comes in keeping the most important information." 2:16
@caseyli5580 5 жыл бұрын
That moment (ha!) when you've been blundering through 5 years of statistics classes and you finally understand what a moment actually is. This was a super clear video and I liked the airport example. btw the "(something I can't understand)" in the subtitles at 9:04 is "rises to a peak in the middle".
@zbuddha 5 жыл бұрын
He is simply saying that the variance of the distribution is largest when p = 0.5. Since the variance of the distribution , V, equals p(1-p) = p - p^2, take the first derivative of this function: dV/dp = 1-2p. Setting it equal to zero gives its optimum: p = 0.5. Since the second derivative of the function is -2 < 0, you know that V gets maximized when p = 0.5.
@scottmacnevin3555 7 жыл бұрын
I found Sanjoy very easy to follow, simple but effective examples provided, thank you!
@JeffBagels 8 жыл бұрын
This guy is awesome. Explains things clearly.
@kevinhevans 5 жыл бұрын
Thought that was a good explanation! I'm just now learning about this in a statistical physics course and it's nice to have a non-physics explanation of it. :)
@avibank 7 жыл бұрын
Appreciate the moments fam.
@tahsinl 6 жыл бұрын
Thanks for the lecture. It was very helpful for studying for my engineering probability exam.
@wisescouncil 4 жыл бұрын
I really love how he casually makes reference to other discipline
@DavidAndrewsPEC 10 жыл бұрын
I found this okay - interesting topic, and a lecturer who knew the topic well enough to teach it in a logical and informative way.
@oViTaLZzHD 5 жыл бұрын
.....but it was no better than ok. Just ok. And if it was any better than ok, I wouldnt admit it, because I am cool, and I have a moustache in my dp. You know what David, fuck you
@dhritishmanhazarika3894 3 жыл бұрын
That's an AI programmed professor. The eyecontact and the way he is speaking. Cool.
@rafaqatmir01 3 жыл бұрын
But his lecture was very cool
@TheAhmedMAhmed 10 жыл бұрын
super cool, super awesome! thanks for the lecture MIT!!!
@shreehimanshu7524 5 жыл бұрын
I want to be teached by this kind of teacher,Respect.
@user-or7ji5hv8y 3 жыл бұрын
This is really well explained. Really insightful.
@vagabond197979 6 жыл бұрын
This was really cool and really helpful. Thanks.
@capitalcitytoastmasters7386 6 жыл бұрын
Loved the explanation!
@Qongrat 3 ай бұрын
The video is great though one little caveat: the mean (μ or x̄ ) is not the same as an expected value (E[X]). Statements like this make things just more difficult and confusing. The (arithmetic) mean is the sum of of all values of set X = { x1, x2, ... xn) divided by the total number of values in the set. It refers to any existing in reality (known or unknown to us) set or lists of values, like samples, populations and so on. Expected value (E[X]) refers to an event in the future and is the most likely probability of what we can expect to get as a value in the said future event. Yes, if you are considering drawing a sample with a large enough n, what you can expect more or less is that E[X] = μ (the mean of the population) given that you are looking at discreet or continuous distributions, but E[X] = p (proportion of the population) if you are looking at binary Bernoulli distribution.
@vaishnavipal2298 2 жыл бұрын
I hope someday MIT OCW uploads a video on Moments and Centre of mass in Calculus not the one in Rotational Dynamics
@dianamorton3716 2 жыл бұрын
Such an interesting explaination! Love it
@julians7785 Жыл бұрын
thank you for the clear explanation!
@TheRealLukeOlsen 5 жыл бұрын
Wow. To calculate the moment of inertia about a parallels axis is actually the 2nd moment subtract the 1st moment squared??? The moment all moments come together.
@chooha 4 жыл бұрын
Beautiful right, I wish I could get that though, I will be coming back to it.
@Bill-lx3cw Жыл бұрын
Thank you - this was very helpful 🥰
@ashutosh.sharma 5 жыл бұрын
what a great teacher
@zes7215 6 жыл бұрын
not often, no such thing as fullx or comx or dependx, can say any nmw is ok
@jaysoncarter8931 7 жыл бұрын
Great video, thank you!
@samirelzein1978 3 жыл бұрын
Very nicely put, thanks!
@bibhabasumohapatra 3 жыл бұрын
what an explanation with Moment of Inertia and traffic! you are such a good explainer but why are you expressionless! don't take it seriously! good job!
@benmackenzie 5 жыл бұрын
This is so good.
@YunusEmre-cv4dy 4 жыл бұрын
It is amazing video
@Thranduil999 10 жыл бұрын
nice video!
@TechnologySandBOX 3 жыл бұрын
Superb!
@simpletwo4768 5 жыл бұрын
why p(x) = 1 in the continuous x example?? Because the area under the curve has to be 1? So if the augment of p(x) expand to [0,2], should p(x)=1/2?? ...Thanks for any replies in advance.
@sidaliu8989 5 жыл бұрын
yes, I think your guess was right. refer to: en.wikipedia.org/wiki/Uniform_distribution_(continuous)
@rudrapratapbansidhar4416 6 жыл бұрын
nice.
@tarkesdora20 4 жыл бұрын
15 min video all important aspects covered
@Vikram-wx4hg 3 жыл бұрын
While I liked the lecture, it did not answer the airport question at all. By the mathematics shown then average and variation of times are same for both the trips. So, the difference is only psychological?
@JaySmith91 10 жыл бұрын
Very informative video series! My main comment would be to suggest that professors refrain from reading verbatim. It makes the content delivery dry and monotonous.
@borg972 5 жыл бұрын
What what do higher moments mean? what can you do with them?
@DimitriBianco 3 жыл бұрын
They describe other features of the distribution. For example the third moment is skew. If the data is skewed, the mean will no longer be at the highest point of the distribution. It also tells you that the mode (number that occurs most often) will be shifted. In a real world application, stationarity is a measure of stability in data. If your moments remain relatively unchanged over time, the data is stationary. Stationarity is a requirement in time series models such as those use for financial data.
@vedant6633 2 жыл бұрын
@@DimitriBianco thanks for info
@qusaysultan2311 5 жыл бұрын
looks young for his age
@nb9797 4 жыл бұрын
1000th like!
@marcustan7236 6 ай бұрын
SUTD gang assemble
@litoboy5 10 жыл бұрын
LOTTERY ?!!!
@loganathan2686 10 жыл бұрын
u guys are racist. look at the value and not the person
@ricardokaka1122 10 жыл бұрын
This professor put me to sleep