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bilibili_data_1302898884_BV1uZ4y1C7SX_p94_BV1uZ4y1C7SX_p94_m4-dialogue_0483358 | [S1] 94, planted strength. [S2] Time to plant a tree. I have to put the shovel back in the shed. [S1] Come here, you little brat. Let me see your boy. I'm going to teach him a lesson. | 25.68 | 2.842938 | 24,000 | audio/en/bilibili_data_1302898884_BV1uZ4y1C7SX_p94_BV1uZ4y1C7SX_p94_m4-dialogue_0483358.mp3 | [
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bilibili_data_1302898884_BV1uZ4y1C7SX_p96_BV1uZ4y1C7SX_p96_m4-dialogue_1040049 | [S1] 96. Family photo. [S2] I like these costumes, Dad. You think this will work? [S1] Of course, my boy. Just play nice with the rabbits. There you go. That is the perfect shot. | 28.28 | 2.890093 | 24,000 | audio/en/bilibili_data_1302898884_BV1uZ4y1C7SX_p96_BV1uZ4y1C7SX_p96_m4-dialogue_1040049.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896241 | [S1] We already knew back then in the late 80s, 1990s, that the universe was expanding. [S2] Right. [S1] And we knew that to see the very first galaxies, and maybe even the first stars, that ever formed in the universe, because of the expansion of the universe, the light from those galaxies is likewise expanded. [S2] M... | 25.64 | 3.118691 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896241.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896242 | [S1] And Uranus. I mean, I just knew that because I knew it would be big enough. I knew that because it was a space telescope, the images would be stable and pristine. And I knew that these wavelengths of light in the infrared had all sorts of interesting molecular signatures- [S2] Mm-hmm. [S1] ... so that we could lea... | 27.72 | 2.939352 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896242.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896243 | [S1] I would like to be an interdisciplinary scientist for this program, to ensure that this telescope will be able to do solar system observations when it is launched. [S2] Mm-hmm. [S1] And in 2003, my proposal was accepted, and that- [S2] Mm-hmm. [S1] ... how it was how I formally became involved in this telescope. S... | 20.48 | 3.115638 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896243.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896244 | [S1] It's, it's different than Hubble. It's a different kind of telescope for a number of reasons. One is, it's a lot bigger than Hubble. [S2] Mm-hmm. [S1] So it's a six and a half meter mirror, the golden mirror of the collecting area, versus Hubble's 2.4. [S2] Mm-hmm. Um, I mean, it's so big that it couldn't be launc... | 29.44 | 3.340408 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896244.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896245 | [S1] Exactly. [S2] But then it had to unfold in space. And I, I remember how nervous people were about this process because it really was something that everything, every single step had to go right. [S1] Not only did the telescope have to fold up, but we, if you look at Webb, it's got this huge contraption underneath ... | 27.24 | 3.097815 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896245.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896246 | [S1] I sure was nervous, just like everybody else. Um, [CLEARS THROAT] there were several single point failures where if that thing didn't unbolt or unfold, we didn't have a, a, a working telescope anymore. [S2] Yeah. [S1] So it was extremely nerve-wracking. But we had many years of testing. | 18.64 | 3.163897 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896246.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896247 | [S1] Because we knew that there was no fixing this telescope. This telescope's not in low Earth orbit like Hubble. The James Webb Space Telescope is a million miles away at a point called the L2 point. [S2] Mm-hmm. [S1] And it was put out there deliberately because it needed to be cold. | 17.76 | 3.159572 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896247.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896248 | [S1] It needed to have a, the sun shield to protect the telescope from the warmth of the sun, the warmth of the earth, and even the warmth of our moon. [S2] Mm-hmm. [S1] So the sun shield is designed to be like a, an umbrella that protects it, a sun umbrella that keeps that telescope super cold. So we couldn't put it i... | 29.4 | 3.237437 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896248.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896249 | [S1] is completely exposed to the elements of space. Most other telescopes, um, have tubes that enclose them, and this one doesn't. The, the mirrors are just sitting out there. [S2] They're just out there. [S1] They're just sitting out there. [S2] So the first deep field from JWST, I think the analogy I heard was that ... | 29.08 | 3.021706 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896249.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896250 | [S1] I heard a grain of sand. [S2] Okay. [S1] Not a grain of rice. [S2] Okay. [S1] But it's the same concept, you know, uh, that, yeah, if you, the piece of sky you see in that picture, if you were, like, standing in your backyard and looking up in the sky, that piece of sky is about the same size as a, as a, as a tiny... | 29.56 | 3.202273 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896250.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896251 | [S1] It is filled with galaxies. [S2] Just thousands and thousands in that one image alone. [S1] Exactly. What I'm waiting for is the James Webb Space Telescope deep field where we stare for days at a dark spot that we don't know where anything is. [S2] Mm-hmm. [S1] What are we going to see? | 21 | 2.933752 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896251.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896252 | [S1] I just think about it appearing so far back in time, to the beginning of, you know, the primordial cosmic murk. [S2] Yeah. [S1] When stars and galaxies are just starting to turn on and... | 14.2 | 3.031802 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896252.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896253 | [S1] I view it as a, as an example of what humanity can do when we work for the greater good. [S2] Mm-hmm. [S1] You know, when we work as teams and we have a goal. [S2] Mm-hmm. [S1] You know, this, this project required thousands of people in multiple countries and multiple states to take this vision and turn it into a... | 29.68 | 3.143875 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896253.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896254 | [S1] You know, from, from right in our local neighborhood, all the way to the edge of the known universe, and everything in between. [S2] Yeah. [S1] It's amazing to me. And everybody had a role to play. I mean, | 12.92 | 2.995172 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896254.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896255 | [S1] You know, both here and Europe, you know, we all worked t- Canada. Canada made the fine guidance sensor that allows us to point this thing. [S2] Mm-hmm. [S1] I mean, it's a truly international effort, um, and it all comes together to create this, this revolution in how we see the cosmos. [S2] Do you have a favorit... | 26.12 | 2.967467 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896255.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896256 | [S1] Stars are being born. [S2] Yeah. [S1] And some of the little poke-y things that, that stick out that give it some of its dramatic structure, you know, those are like, that's star birth in the making. | 12.64 | 3.379851 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896256.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896257 | [S1] And I get, I think that's just so cool. And particularly when we use our infrared cameras, we can look inside some of those knobs and, and see the stars that are being born. [S2] Mm-hmm. [S1] And in some places, uh, just, uh, like the Orion Nebula, there was just an image released of the Orion Nebula. [S2] Mm-hmm.... | 28.24 | 3.132774 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896257.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896258 | [S1] What you learn from James Webb Space Telescope is that in the regions where they are interacting and overlapping, those regions light up in the infrared. Those are places where the dust and the gas and the existing stars of those other galaxies, when they are interacting, they are forming new stars. They are creat... | 29.48 | 3.168993 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896258.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896259 | [S1] in that image. So, yeah. [S2] And I just wonder, like, what are, what's missing from that picture? What is, what can JWST fill in? I mean, how much more color can it add? [S1] Um, what JWST adds to our ongoing story is it, it adds new wavelengths of light that we haven't had the sensitivity to study. And different... | 28.12 | 3.040514 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896259.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896260 | [S1] That tells you what molecules are there. [S2] Sure. [S1] Not only does it tell you what are there, it tells you their temperature. [S2] Mm-hmm. [S1] It can tell you their pressures. [S2] Mm-hmm. [S1] By tracking, um, carefully these lines in the spectrum, you can determine the motions of this material. And so we d... | 29.88 | 3.194287 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896260.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896262 | [S1] What's your favorite? [S2] What's, what's your favorite? [S1] Oh, I don't know. [S2] [LAUGHS] [S1] I've got a couple of favorites. [S2] Yeah? [S1] I think a lot of astronomers, a, a favorite system right now is the Trappist-1 system. [S2] Yeah, tell me about it. | 13.32 | 2.954172 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896262.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896263 | [S1] Yeah, Trappist-1 is, um, that's the name of the star. Well, it's, Trappist is the name of the survey, right? [S2] Yeah. [S1] But it looked at this star and, um, it discovered, um, that there are at least seven planets orbiting this star, and most of those planets seem to be Earth-sized. In the Trappist-1 system, s... | 27.96 | 3.226811 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896263.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896264 | [S1] that water could be liquid on the surface of them. We call that the habitable zone. [S2] Right. [S1] And you and I can have a long talk about what habitability actually means. But, you know, in our solar system, at least on our Earth, the only place that we know life exists is a lot of water. [S2] Yeah. [S1] And s... | 21.4 | 2.826982 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896264.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896265 | [S1] Okay, so let me answer the second question first. Um, this question of, is there alien life out there? I usually break it up into two things. One is a sp- thought experiment about the size of the universe, the scale of the universe, just how many stars there are in our galaxy. [S2] Yeah. [S1] And then how many gal... | 27.84 | 3.381773 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896265.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896266 | [S1] And we talk about whether or not life could have formed over the billions of years that our, our universe has existed with these billions of galaxies, each of which has billions of stars. I say life has to exist somewhere out there. [S2] Mm-hmm. [S1] Somewhere. Has to be out there. [S2] Mm-hmm. | 17.72 | 3.126696 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896266.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896267 | [S1] Does that mean that aliens have come to Earth and visited us? No. That's a totally separate question. [S2] Yeah. [S1] I just, they're not, it's not a related question, all right? That's a more psychological question. [S2] [LAUGHS] [S1] I'm more interested in the science aspect of the question. I, I think we need t... | 24.56 | 2.914975 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896267.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896268 | [S1] ... orbital imaging, um, it's clear that there's evidence that at one time, there was liquid water on the surface of Mars. There's, you know, there's sedimentation, there's a chemical evidence, there's, you know, actually water trapped in the ices in the poles of Mars right now. [S2] Mm-hmm. [S1] And so, uh, | 19.72 | 3.047982 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896268.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896269 | [S1] It, it could very well be that at some time in the past, that planet had liquid water and may have had the conditions for life to form. We don't know. It could be that life formed there first and transmitted itself inward to us. We could be Martians. [S2] We could be Martians. [S1] I don't know. You know, we don't... | 27.08 | 3.180508 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896269.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896270 | [S1] Crazy. Think about it. [S2] It's kind of mind-boggling to think about. [S1] Yeah. Um, the question is, could life form in that water? And, and it gets back to what are the ingredients you need for life? You need water, but you also need some kind of an energy source. You need some kind of a surface on which life c... | 29.52 | 3.138391 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896270.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896271 | [S1] And we also know that Europa is warm. [S2] Mm-hmm. [S1] Now why? Why would this moon out there at Jupiter's distance, why would it be warm, right? | 10.08 | 3.18923 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896271.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896273 | [S1] And that repeating warms the planet. Um, I used to illustrate this for kids with, um, like old credit cards. If you- [S2] Oh, yeah. [S1] ... I took an old credit card and you bend it, bend it, bend it, bend it, bend it, and you feel where you're bending, it's warm. [S2] It gets warm. [S1] It's really the same proc... | 23 | 2.934465 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896273.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896274 | [S1] We have the rocky surface deep inside, and we have warmth. You know, we've got this energy source thing. So, is it possible that life is formed there? [S2] What? Man. [S1] Sure. | 12.8 | 3.036248 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896274.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896275 | [S1] My favorite moon is Triton. [S2] Oh, it's a pretty good one. [S1] It's not the right one, though. [S2] I was gonna say Iapetus. [S1] Oh, no, no, no, no, no, no. We're gonna have to have a long conversation about that. [S2] Okay. Okay. Tell me why Triton is better than Iapetus. [S1] All right, so, Triton. Triton is... | 29.52 | 2.968743 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896275.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896276 | [S1] And it's a big moon. I mean, if you want Pluto to be a planet, I don't know where you s- stand on that issue, but Triton is a twin to Pluto, all right? So it's like a planet in orbit around another planet. [S2] But it's going backwards. [S1] But it's going backwards around the planet. And when Voyager flew by in 1... | 24.24 | 3.192189 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896276.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896277 | [S1] a good view of one half of it. And it's got remarkable terrain. [S2] Hmm. [S1] And it has active cryovolcanoes on it. There are volcanoes, ice volcanoes erupting on Triton, like in real time. So that's pretty amazing. [S2] [LAUGHS] | 17.72 | 3.232609 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896277.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896278 | [S1] So, I asked my history teacher instead, and she did write a letter, and I did get into MIT. [S2] Mm-hmm. [S1] And when I brought back my acceptance letter and showed it to my chemistry teacher, look, I got into MIT, he said, "It's only because you're a woman. They have quotas to fill." | 17.24 | 3.195923 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896278.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896279 | [S1] This is in 1978, when people said things like that to your face. Um, that made me angry, more than anything. [S2] I'm super- [S1] So, I was determined to go to MIT and graduate, you know. [S2] What are some of the most nagging, unanswered questions in your mind that exist in astronomy, any field in astronomy? Coul... | 27.84 | 3.301671 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896279.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896280 | [S1] is an area that is very, very interesting right now. And of course, that's why James Webb Space Telescope was built, to add to a, to a piece to that story. [S2] Mm-hmm. [S1] I think I'm also interested in how our planetary system that we live in, how did it in particular come to be- [S2] Mm-hmm. [S1] ... and how d... | 29.12 | 3.278979 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896280.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896281 | [S1] The only system that we know is inhabited, right? This, our solar system. [S2] Right. [S1] Is it required that you have giant planets in the outer system and small planets in the inner solar system to make habitability, or is it just by happenstance? Did you have to have a Jupiter to make it habitable? Did you hav... | 27.88 | 2.951105 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896281.mp3 | [
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bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896282 | [S1] I mean, I, that's so interesting. And, um, it, it, it touches us as humans. Like, how did we come to be? It's, it's part of our story. It's part of our life story. So I'm very interested in that question as well. And we still have so many observations left to make. [S2] [LAUGHS] [S1] Both within our solar system a... | 27.6 | 3.046639 | 24,000 | audio/en/bilibili_data_1303874431_BV1ud4y17776_BV1ud4y17776_m4-dialogue_0896282.mp3 | [
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bilibili_data_13166851_BV1rP411w7kC_BV1rP411w7kC_m4-dialogue_0483209 | [S1] I'm beside myself. [S2] Yeah. [S1] Really. Um, yeah, it's, it's actually, uh, it's a concept record, but it's my first directly autobiographical album in a while because the last album that I put out was, um, a re-record of my album Red. So that has some space. You know, you, I wrote that stuff a decade ago. Folkl... | 29.8 | 3.371757 | 24,000 | audio/en/bilibili_data_13166851_BV1rP411w7kC_BV1rP411w7kC_m4-dialogue_0483209.mp3 | [
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bilibili_data_13166851_BV1rP411w7kC_BV1rP411w7kC_m4-dialogue_0483214 | [S1] No, I- [S2] And so very lucky to have collaborated with her on that. And Dylan was actually in the studio with me and Jack because we re- a lot of the time we record at his place and, um, and Dylan was just hanging out, like drinking wine with us and listening to stuff. And, and he was just trying out the drum kit... | 20.68 | 3.048749 | 24,000 | audio/en/bilibili_data_13166851_BV1rP411w7kC_BV1rP411w7kC_m4-dialogue_0483214.mp3 | [
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bilibili_data_13166851_BV1rP411w7kC_BV1rP411w7kC_m4-dialogue_0483228 | [S1] You had a pizza party. [S2] Mm-hmm. [S1] And I went to it, and I ended up in a corner talking to Mike Birbiglia and his wife Jen, who's a poet. [S2] Yeah. [S1] All night, they're the most wonderful people. And I just remember thinking, 'cause I'd seen Mike in things, and I'd seen some of his, you know, he does the... | 26.8 | 3.31882 | 24,000 | audio/en/bilibili_data_13166851_BV1rP411w7kC_BV1rP411w7kC_m4-dialogue_0483228.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302716 | [S1] ... survive or die with a given disease. So it's a very commonly occurring problem and very important. So we're going to spend some time today on this, actually in the, in the next, uh, set of lectures on classification. And Trevor and I are both here. Trevor's going to give the m- uh, most of the talk and I'm goi... | 26.12 | 2.965027 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302716.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302717 | [S1] The first thing we do is just show you what, what categorical variables look like. I'm sure you know. So for example, eye color, that takes on three values, brown, blue, and green. Those are, those are discrete values. There's no ordering. They're just three different values. Email, we've talked about already, is ... | 21.76 | 3.246749 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302717.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302718 | [S1] Oh, you tell me, Rob. [S2] Okay. Well, let's see, a box plot, what's, what's indicated there, Trevor, you can point, the, the black line is the median. [S1] It is a, it is a black line, that's a median. [S2] So that's the median, the medium for the, the yes, for the people, the median income for people who have de... | 28.52 | 3.204741 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302718.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302719 | [S1] So really a good summary of, of the distribution, um, of income for those in category S. What about these, these things at the end, Rome? [S2] Okay, I think they're called hinges. [S1] They are called hinges. [S2] And those are the ranges, are they, or the, approximately the ranges of the data? | 15.4 | 3.334861 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302719.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302720 | [S1] Who invented the box- [S2] John Tukey. [S1] John Tukey, one of the most famous statisticians. He's no longer with us, but he's left a, a big legacy behind. | 8.8 | 3.198794 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p14_BV11t411A7Ym_p14_m4-dialogue_0302720.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p15_BV11t411A7Ym_p15_m4-dialogue_0817888 | [S1] Well, the popular way is to use maximum likelihood. Maximum likelihood was introduced by who, Rob? [S2] Me, actually. [S1] Oh, yeah. [S2] Just last week. [S1] Did you reinvent it, or? [S2] I didn't realize it was actually, yeah. It, um, I, the correct answer is Fisher, back in the 1920s, Ronald Fisher. [S1] Fisher... | 28.28 | 3.017613 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p15_BV11t411A7Ym_p15_m4-dialogue_0817888.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p15_BV11t411A7Ym_p15_m4-dialogue_0817889 | [S1] And in this case, this is what it produced. The coefficient estimates were -10 for the intercept and .0055 for the slope for balance. That's beta and beta zero. It also gives you the, so they're the coefficient estimates. It also gives you standard errors for each of the coefficient estimates. It computes a Z-stat... | 29.92 | 3.265341 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p15_BV11t411A7Ym_p15_m4-dialogue_0817889.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p15_BV11t411A7Ym_p15_m4-dialogue_0817890 | [S1] ... the, a curve? [S2] Yes. [S1] And is that how you found the ... I was wondering how you found the parameters for that curve. Is that how you found them? [S2] That's exactly right, Rob. [S1] Oh. [S2] So, this curve over here is the curve corresponding to those estimates that we just produced in the table. And yo... | 26.96 | 3.277783 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p15_BV11t411A7Ym_p15_m4-dialogue_0817890.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p16_BV11t411A7Ym_p16_m4-dialogue_0041755 | [S1] So before, when we just measured student on its own, it had a positive coefficient. But when we fit it in the multivariate model, the coefficient's negative. Do you think this is an error, Rob? [S2] Uh, I don't think so. [S1] So how could that happen? [S2] Well, we remembered last time we talked about in regressio... | 29.92 | 3.241801 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p16_BV11t411A7Ym_p16_m4-dialogue_0041755.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p16_BV11t411A7Ym_p16_m4-dialogue_0041756 | [S1] We're not really trying to predict the probability of getting a heart disease. What we're really trying to do is to understand the role of the risk factors in, in, you know, in the, in, uh, to, in the risk of, of heart disease. And actually, this study was, uh, was an intervention study aimed at educating the publ... | 29.84 | 3.15279 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p16_BV11t411A7Ym_p16_m4-dialogue_0041756.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p16_BV11t411A7Ym_p16_m4-dialogue_0041757 | [S1] Um, you know what they call a barbecue in South Africa? [S2] No. [S1] Brie Flakes. [S2] Okay. [S1] Every South African loves a brie flake and their bull tongue. So here's the result of GLM, um, for the heart disease data. And here I actually show you some of the code used to fit it. And we get it, we'll get into t... | 29.92 | 3.082396 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p16_BV11t411A7Ym_p16_m4-dialogue_0041757.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197517 | [S1] Hi, I'm Trevor. No, I'm Rob Tipshrani. [S2] And I'm Trevor Hastie. And welcome to our course on statistical learning. [S1] This is the first online course we've ever, we've ever given, and we're really excited to tell you about it. [S2] And a little nervous, as you can hear. [S1] So, uh, by way of background, what... | 29.92 | 3.41499 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197517.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197518 | [S1] Um, but in the 1980s, people in, in computer science developed their field of machine learning. Uh, especially neural networks became a very hot topic. I was at University of Toronto and Trevor was at Bell Labs. [S2] And one of the first neural networks was developed at Bell Labs to solve the, the zip code, uh, re... | 25.52 | 3.360267 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197518.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197519 | [S1] Uh, very, uh, very well. Matter of fact, he got all the, the, the, the Senate races right, and the, uh, the, the presidential election, he, he predicted very, very accurately using statistics, using carefully div- uh, carefully sampled data from various places, some careful analysis. He did an extremely accurate j... | 25.44 | 3.338238 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197519.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197520 | [S1] And this is looking at, this graph has the, a log periodograms of, uh, for two different phonemes, the power at different frequencies for two different phonemes, uh, A, A, A and A-O. How do you pronounce those, Trevor? [S2] A-A is odd and A-O is ought. [S1] As you can tell, Trevor talks funny, but hopefully during... | 23.08 | 3.369083 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197520.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197521 | [S1] And these, these data come from a region of South Africa where the heart, the risk of heart, uh, disease is very high. It's over, uh, over 5% for this age group. The people, the, the, the, especially men around, they eat lots of, uh, these were men, um, they eat lots of meat. They have meat for all three meals. An... | 27.12 | 3.420866 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197521.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197522 | [S1] Our next example is email spam detection. Now, as everyone who uses email, and spam is definitely a problem. Uh, and so, um, spam filter is a very important application of Cisco machine learning. The data in this, on this table actually, um, I think it's from the, the, maybe the late '90s. Is that right? [S2] Befo... | 20.64 | 3.232604 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197522.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197523 | [S1] It's from Hewlett Packard. So this is a, a person named George, who worked at Hewlett Packard. So this was early in the days of email where, as well, spam was also not very sophisticated. So what we have here is data from over 4,000 emails sent to an individual named George at HP Labs. Each one's been hand-labeled... | 22.8 | 3.241064 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197523.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197524 | [S1] Uh, remove. Okay. So I guess it probably said something like, "Don't remove." Is that right? [S2] I think it says, uh, if you want to be removed from this list, click. [S1] I see. [S2] That's usually a spam. [S1] Right. So the goal was that in, if it, and we'll talk about this example in detail, to use the, the, t... | 26.64 | 3.041167 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197524.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197525 | [S1] Actually, I remember the first time, Trevor, that we worked on a machine learning problem. It was, it was this problem, and you were working at Bell Labs. I visited Bell Labs. And you'd just gotten this data, and you said, "These people in artificial intelligence are working on this." And that we thought, "Oh, let... | 27.16 | 3.372746 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197525.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197526 | [S1] So here we see the, the three, three of the variables that affect income. And again, the goal is we'd use regression models to try and understand the roles of these variables together and see if there's, you know, if there's interactions and so on. [S2] And the last example is, um, Landsat images of, of land use, ... | 29.36 | 3.330702 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197526.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197527 | [S1] This is before it's developed taste. [S2] When did that happen? [S1] [LAUGHS] [S2] I didn't, I didn't see the, uh, news memo. Okay. So, um, here are, uh, these are from landslide images. So let's start here in the, in this panel. So this is, uh, again, uh, a rural area of Australia where the, uh, land use has been... | 29.56 | 3.059123 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197527.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197528 | [S1] And- [S2] Pixel by pixel, right? [S1] Pixel by pixel. [S2] Yeah. [S1] Although we might want to use the fact that nearby pixels are more likely to be the same land use than ones that are far away. And we'll talk about classifiers. I think the one we use here is actually nearest neighbor. It's a very simple classif... | 21.48 | 3.109707 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p1_BV11t411A7Ym_p1_m4-dialogue_0197528.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034573 | [S1] The other thing- [S2] You're going to give my, going to give my entire lecture? [S1] Oh, I'll try not to, Rob. Just in case you miss out some of the salient points. The other thing we're going to look at is standard errors of estimators. Sometimes our estimators are quite complex and we'd like to know the standard... | 26.92 | 3.170249 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034573.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034574 | [S1] about fitting a polynomial with higher and higher degree. [S2] Right. [S1] You can see our model complexity increases with degree. [S2] So we move to the right, we'd have a higher complexity, higher order of polynomial. The prediction errors on the vertical axis. And we have two curves here, the training error in ... | 28.8 | 3.124931 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034574.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034575 | [S1] This is simply a, a one stage process. We divide it in half, train on one half, and predict on the other half. [S2] It seems a little wasteful if you've got a very small data set, right? [S1] Yeah, that is wasteful. And, uh, as we'll see, it's cross validation will, uh, remove that waste and be more efficient. But... | 29.28 | 3.233212 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034575.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034576 | [S1] Um, and that's likely to be quite a bit higher than the, uh, the error for a training set of size N. And- [S2] Why, Rob? [S1] Why? [LAUGHS] That was my question. Okay. | 14.16 | 2.998149 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p25_BV11t411A7Ym_p25_m4-dialogue_1034576.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721212 | [S1] So here's the cross validation error rate. Um, basically, this is the mean square error we get by applying, uh, the, the, we, we fit to the, the K-1 parts that don't involve part number K. That gives us our fit, Y-I hat, uh, for observation I. [S2] It's four-fifths of the data in this case. [S1] Right. And then we... | 28.32 | 3.007559 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721212.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721213 | [S1] ... a tenfold cross validation. Now, again, it's, it's also showing the minimum around two, but it's, uh, there's not the, what we're seeing here is the tenfold cross validation as we, as we, uh, take different partitions in, uh, into ten parts of the data, and we see there's not much variability. They're pretty c... | 28.44 | 3.163088 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721213.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721214 | [S1] This is figure 5.6 from the textbook, and this is the, uh, simulated data example, which was figured from figure 2.9 of the book. Um, just recall that this is, uh, smoothing splines in three different situations. In this case, the true curve, true error curve is the blue curve. Uh, and again, these th- three diffe... | 28.24 | 3.296734 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721214.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721215 | [S1] So we can get a, we can get a, a very big test set. [S2] Exactly. [S1] And estimate the error exactly. [S2] Um, Lee-Van-El cross validation is the, uh, the black broken line and the orange curve is 10-fold cross validation. So we can see, what do we see? Well, here we see that, um, | 17.4 | 3.198921 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721215.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721216 | [S1] and the, the true error curve is minimized around, I guess maybe three. [S2] Although those, those error curves are fairly flat. [S1] Yeah. [S2] So, there's obviously a, a high variance in, in where the minimum should be. [S1] Right. And then- [S2] It doesn't really matter where, you know. [S1] That's right. It's ... | 29.36 | 3.170771 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721216.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721217 | [S1] like a weighted average in that formula, right? There's NK over N. [S2] Well, do you want to explain that? [S1] Because the, the, each of the folds might not be exactly the same size. So we, we actually compute a weight which is proportionate to this, which is the relative size of the, of the fold, and then use a ... | 27.64 | 3.043774 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721217.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721218 | [S1] Well, I wonder why. Well, the thing is we, we, we compute in a standard errors if these were independent observations, but they're not strictly independent. Error sub K, um, overlaps with error sub J because they share some training samples. [S2] Right. [S1] Um, so there's some correlation between them, but we use... | 20.44 | 3.097408 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p26_BV11t411A7Ym_p26_m4-dialogue_0721218.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407418 | [S1] And then we, we, we use 100 predictors with the cla- uh, uh, we use them as, in a classifier, such as a, a low-jump model using y- uh, only these 100 predictors, and we omit the other 4,900. So that's not unreasonable. We have a building a classifier. For example, maybe we don't want to have to deal with 5,000 pre... | 28.84 | 2.897981 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407418.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407419 | [S1] leaving out the first filtering step and giving it a very, uh, cherry-picked set of predictors in the second set. [S2] Is it- [S1] In the second step. | 7.8 | 3.01742 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407419.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407420 | [S1] And now we can do whatever we want on the other four parts. We can filter and fit or however we want. And fi- when, the, when, when, uh, when we finished our fitting, we'd then take the model and we'd predict the response for the left out part. The key point being, though, that we, we form the folds before we filt... | 27.32 | 3.099902 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407420.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407421 | [S1] He didn't agree, and his supervisor didn't even agree, who also, I will not name the person. [S2] Wasn't me. [S1] It wasn't you. Well, the supervisor said, "Well, maybe you're right, but you're really being picky. You're splitting hairs here. It's not gonna make much difference." And I said, "Well, I think it migh... | 19.84 | 3.01147 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407421.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407422 | [S1] Uh, that was, again, it's, it happens and Trevor and I have talked about this a lot and other people have written papers about this error, but people continue to make this error in cross-validation. [S2] So that's a big heads up. [S1] Yeah. [S2] And of course another heads up is not have Rob Tipton- [S1] [LAUGHS] ... | 28.04 | 2.858651 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p27_BV11t411A7Ym_p27_m4-dialogue_0407422.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p28_BV11t411A7Ym_p28_m4-dialogue_0041758 | [S1] Welcome back. In the last section, we talked about cross validation, um, for, for the estimation of test error for supervised learning. Now we'll talk about a closely related idea called the bootstrap. [S2] It's a powerful method for, for assessing uncertainty in estimates. In particular, for getting, getting idea... | 28.16 | 3.361954 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p28_BV11t411A7Ym_p28_m4-dialogue_0041758.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p28_BV11t411A7Ym_p28_m4-dialogue_0041759 | [S1] ... the best amount, um, proportion to put into, uh, into X, and the remain goes into Y, to minimize the total variance. Okay, so- [S2] Those are population quantities, aren't they? [S1] Those are population quantities. So- | 13.68 | 3.17336 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p28_BV11t411A7Ym_p28_m4-dialogue_0041759.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p28_BV11t411A7Ym_p28_m4-dialogue_0041760 | [S1] And we do this a thousand times. We take the standard, standard error of those. Well, actually, let's, if you do this a thousand times, we'll go to the, look at the histogram in a couple of slides. This, this histogram on the left shows the 1,000 values over 1,000 simulations from this experiment. Each one is a va... | 25.48 | 3.189759 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p28_BV11t411A7Ym_p28_m4-dialogue_0041760.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p2_BV11t411A7Ym_p2_m4-dialogue_0616554 | [S1] Now, um, unsupervised learning is another thing, topic of this course in which- [S2] That's how I grew up. [S1] I see, that's the problem. Okay. Well, so in, in unsupervised learning, now in the kindergarten, now Trevor's in kindergarten, and the child was not, Trevor was not given examples of what, uh, house and ... | 29.12 | 3.316838 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p2_BV11t411A7Ym_p2_m4-dialogue_0616554.mp3 | [
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bilibili_data_13232720_BV11t411A7Ym_p2_BV11t411A7Ym_p2_m4-dialogue_0616555 | [S1] And so you can think of this as having a, a very big matrix, um, which is very sparsely populated with ratings between one and five. And then the goal is to try and predict, as in all recommender systems, to predict what the customers would think of the other movies based on what they'd rated so far. So Netflix se... | 28.72 | 3.390262 | 24,000 | audio/en/bilibili_data_13232720_BV11t411A7Ym_p2_BV11t411A7Ym_p2_m4-dialogue_0616555.mp3 | [
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