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.
@CuongHut3 жыл бұрын
"Throwing away information is the only way to fit the complexity of the world into our brains " - Sanjoy Mahajan
@wentaofeng55487 жыл бұрын
i really respect my professor, but man the guy in video is 100 times better in every aspect.....
@benmackenzie5 жыл бұрын
100%
@adiosm572 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
@caseyli55805 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".
@zbuddha5 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.
@scottmacnevin35557 жыл бұрын
I found Sanjoy very easy to follow, simple but effective examples provided, thank you!
@JeffBagels8 жыл бұрын
This guy is awesome. Explains things clearly.
@kevinhevans5 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. :)
@avibank7 жыл бұрын
Appreciate the moments fam.
@tahsinl6 жыл бұрын
Thanks for the lecture. It was very helpful for studying for my engineering probability exam.
@wisescouncil4 жыл бұрын
I really love how he casually makes reference to other discipline
@DavidAndrewsPEC10 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.
@oViTaLZzHD5 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
@dhritishmanhazarika38943 жыл бұрын
That's an AI programmed professor. The eyecontact and the way he is speaking. Cool.
@rafaqatmir013 жыл бұрын
But his lecture was very cool
@TheAhmedMAhmed10 жыл бұрын
super cool, super awesome! thanks for the lecture MIT!!!
@shreehimanshu75245 жыл бұрын
I want to be teached by this kind of teacher,Respect.
@user-or7ji5hv8y3 жыл бұрын
This is really well explained. Really insightful.
@vagabond1979796 жыл бұрын
This was really cool and really helpful. Thanks.
@capitalcitytoastmasters73866 жыл бұрын
Loved the explanation!
@Qongrat3 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.
@vaishnavipal22982 жыл бұрын
I hope someday MIT OCW uploads a video on Moments and Centre of mass in Calculus not the one in Rotational Dynamics
@dianamorton37162 жыл бұрын
Such an interesting explaination! Love it
@julians7785 Жыл бұрын
thank you for the clear explanation!
@TheRealLukeOlsen5 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.
@chooha4 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.sharma5 жыл бұрын
what a great teacher
@zes72156 жыл бұрын
not often, no such thing as fullx or comx or dependx, can say any nmw is ok
@jaysoncarter89317 жыл бұрын
Great video, thank you!
@samirelzein19783 жыл бұрын
Very nicely put, thanks!
@bibhabasumohapatra3 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!
@benmackenzie5 жыл бұрын
This is so good.
@YunusEmre-cv4dy4 жыл бұрын
It is amazing video
@Thranduil99910 жыл бұрын
nice video!
@TechnologySandBOX3 жыл бұрын
Superb!
@simpletwo47685 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.
@sidaliu89895 жыл бұрын
yes, I think your guess was right. refer to: en.wikipedia.org/wiki/Uniform_distribution_(continuous)
@rudrapratapbansidhar44166 жыл бұрын
nice.
@tarkesdora204 жыл бұрын
15 min video all important aspects covered
@Vikram-wx4hg3 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?
@JaySmith9110 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.
@borg9725 жыл бұрын
What what do higher moments mean? what can you do with them?
@DimitriBianco3 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.
@vedant66332 жыл бұрын
@@DimitriBianco thanks for info
@qusaysultan23115 жыл бұрын
looks young for his age
@nb97974 жыл бұрын
1000th like!
@marcustan72366 ай бұрын
SUTD gang assemble
@litoboy510 жыл бұрын
LOTTERY ?!!!
@loganathan268610 жыл бұрын
u guys are racist. look at the value and not the person