아래 영어를 한글로 번역해주세요. 

you you by the hello I'm I'm with by the anyone undergraduate students here so everyone's are graduate students as far as you know we are about 100 people so this is a really big course I'm sorry but that's how it works so you can read it I will upload this to the iCampus so basically every course will be recorded through the zoom and then I will upload the video after the class probably one day after the class so you can take a look again I strongly recommend you to come to the class offline and obviously I will use the electronic attendance check system to check your attendance and that's going to be part of your grades not that much but small but still that's very important but just in case you have some occasion and you cannot attend the course offline you can use online video to study by yourself sounds good so this is quite important you have to know calculus and linear algebra and also probability ANSI viene very important I will try my best m to explain some necessary subjects before Iが going to the sólo concept if you don t know those if you haven't taken those courses risk, but you shouldn't complain that the course is too hard. So does it make sense? You can study these topics first and then take this course later. So this course is it will be a there is going to be a lot of mathematics and also some probability and linear algebra. So hopefully you're familiar with these topics already. Cool. And we are going to have programming homework. So the title, I mean, this course is Cornerstone, means you have to do some programming assignments. So you have to do some programming assignments. And I'm not going to do any project because I don't want to bother you. You guys are all graduate students, you're busy, so I don't want to, you know, give more pressure to your workload. But we need to do some programming assignments. Then every programming assignment will be based on the Python. So if you don't know about Python, you shouldn't take this course. Sorry. That's good. Any questions about background? And yeah, this is English only, I explained already. And exam is also English. You have to write answers in English. Everything is English except for the English. So I'll just do a little bit of live interaction with you guys. So you can ask questions in Korean. But I will use English. But sometimes I can use Korean to explain better. But I guess maybe 99% will be English. I know my English is not really good. But I'll try to slow down. So that you can understand what I'm trying to say. That's the most important thing. So I changed my mind this morning. So you're going to have four program assignments. 10% 10% which we Masters. Yes. But if you want to do so and we have, again which areолжmetros me a little quarter and you are going to have more time, you can put in minutes on, that you think how much questions you want in this course. Yeah. If you could give a free one for our convener. If you can, you can give. Because only the one question can you get a free pass. Because I will try to. Yes.데 There is an именно one more question that I write after the presentation. Go ahead. Tell us, please. Oakland, feels like isn't there a lot of speech. system in the back. So before you come to the seats, you have to check. You know, you know how to use the app, right? So, so I've done this for about two or three years already. And I know the first class, there are a lot of people like 100 students. And sometimes some students just check there and then just go away. It's up to you. But sometimes I call your name, maybe once or twice or semester. And if you try to cheat, maybe you'll be in trouble. But do your best. And homework 40% one for four program assignments, so 10 points for homework. And so, so I, I want to do a lot of homeworks. But the thing is, it can't do you know, hundreds students homework, like, you know, every week, we don't have that much research. So if you want to learn more, you can do yourself, right? You can study by yourself. If I don't ask you to do something. But there will be four homeworks. And final exam, 15%. And there's no midterm. It's difficult for you. Oh, yeah. So TA first, we have two TA. Is it here? Younghyun? No, no. So you should contact TA first. And then I will, you know, I will, I mean, they are my graduate students. And I will talk with them on a weekly basis. So I will know the issues and the homeworks, whatever. But please use email. And with this subject line, start with this, this prompt. Otherwise, your email may not be sent to TA only. Or you will copy the question. The smallest I Hö oil aperture is one times in a year. Sye Ang Uy Jung Sye Everybody is trying to save herself. So let's just fart and watch for one more few minutes, then we'll come. That's important. But first, what you don't like about MAESAS or things like Algebra to me is, the problem with some things is that, if you don't prepare for your exam, that will bring a high risers time. that reduces parking too. Well, I'd love to. Sther if you're not ready to go. So if you were learning personal
 what you need to do. to basic machine learning and foundation behind the method. And so first we start from linear regression and also some optimization method. And then we are going to look at some probabilistic interpretation. And we're gonna learn how to do classification, which is logistic regression. And then we are going to look at regularization and also some generative learning algorithm. And midterm break, we don't have any midterm exam. So just you can take some break. And two neural networks and also SDM support vector machine. And then unsupervised learning, K-means Gaussian mixture model and expectation maximization algorithm. And then finally we are going to do some dimensional reduction, PCA and some others. So that's my schedule, but it can be changed over time depending on the schedule and also some other issues. So from this year, the 16 weeks for every course and the final week, there's gonna be final exam. And yeah. So now all the data in detail has been collected and updated. And I've actually done thisormalization through Real burnt error definition, and if, in other words in 60 days, we've done the sorry точa or, let's say Turkey curve we couldn't give a enriched является this particular analysis directly away from every class. So the final analysis about nadzieję or homveda will be completely and ப how ingrained in the actual embedded nature. I'll finish this again. seven eight zero, this is also great. They also have YouTube lectures. So if you want, you can take a look. And also a lot of textbook from the classical textbook from Bishop and Murphy and others. Any questions about course logistics? Yep. Oh, have you taken linear algebra course? I think that should be okay. Yeah, I think that should be fine. But again, it's up to you. So if you think you're not really familiar, you can study by yourself before the course. For starts. Any other questions? Yeah. Yes. Yes. So every slice will be already after the class. I will try my best to upload the lecture slide before the class. I cannot guarantee. That's right. Any other questions? No. My goal is to finish today by one. And the thyme basically had run around the class. So I'm dumplings that I was saying yes to this! Okay. So, I'm going to part two. Now I feel it's being a littleкий but if you understand English as well, you're going to very good. I am very pleased to receive a masker. But if I haven't correctly chosen the person that is going to ask so today. Okay. This earlier in today's class you're in this class? Yes. This first session you are in a student's classroom. Uh, This is a little bit advanced and undergraduate course. The contents are about the same. So I don't recommend you to take both courses. So usually first week, I don't do any lecture, but I introduce some examples of what we can do with machine learning. To motivate you to study hard. So obviously some of the example that I'm showing right now is quite, may not be really relevant to what we are going to study in this class because this course is about very basic methods before the deep learning. But most of the recent machine learning breakthrough is because of the deep learning. But the contents that we are going to learn in the course is literally is a foundation. So you should know the concepts actually before you study the deep learning and most recent techniques. But most of the example that I'm showing is basically, all deep learning. And we are going to cover some neural network, which is deep learning for two weeks, I guess. But that's really basic stuff. And if you heard about, you know, Transformer or CNN, whatever, you know, fancy architecture that people are talking about, it's not covered by this course. If you want to study that course, the topics, you have to take the courses. We have a lot of course offerings in SKKU. And also, there are a lot of, you know, YouTube material you can take a look. So object detection, it's very simple concept, you give it an image, then machine learning or AI system is going to produce the bounding boxes, and they then stop the face. So that's the basic concept. And also, there are lots of other things you can take a look at. So object detection, it's very simple concept, you give it an image, then machine learning or AI system is going to produce the bounding boxes, and there are a lot of things that we are going to study in this course. and also labels and so on. There are a lot of information here, right? There's car here and the stop sign, and then you can also draw, you know, road line in the road. So this object detection technique is core technique of the self-driving cars, and every self-driving car company are using this technique. And another example, it's called image segmentation, and given an image, and you are going to label every pixel. So the AI, the machine learning is going to tell you which pixel, which category this pixel belongs to. For example, this is person, and this is car, and this is tree, and this is load, and so on. So image segmentation is also being used in many practical application, including self-driving cars and others. And human pose estimation, so many practical applications are related to what human do, and we can estimate the skeleton of the human pose so that we can use this technique to interact, I don't know, some games or some metaverse, whatever. So detection, segmentation, and pose estimation, those are, those are some of the things that machine learning is going to be able to do. So I think that this is something that machine learning system recognize some concept in the image. But nowadays, as you all know, generative model is getting really important, and these images are not the real images. This is images produced by machine learning model, and this is the model that we're using. So this is the model that we're using. It's actually, this is quite old. It's already 2019, maybe five, five years back. It's still very clear and sharp and realistic. It's called StyleGAN, I'm not sure if you heard about. This is called dog ball. It's a dog and it's a tennis ball. It's a ball. It's a ball. It's a ball. It's a ball. It's a ball. And obviously this image doesn't exist in the world, right? So this is kind of evidence that machine learning system can actually synthesize some new concept, whether they memorize the training data. Another example, this task is called novel view synthesis. Basically you take a pictures of the scene and then some machine learning system. You take these images, for example here, maybe around 20 or 30 images around the scene from different view perspective. And then the machine learning can generate the infinite number of views. So basically it can generate, you know, a novel view images, even though you never seen that images from your training, the pictures you take. It's quite realistic. You can also express some reflectance here. And also ML is playing a really important role in medical images as well. Nowadays, medical image analysis, the accuracy is going to be more than, higher than 95% or something. It's almost similar to what actual doctors said. And there are many startup and big companies to try to apply these techniques to, in real medical situation. There are a lot of reports that AI can actually detect some cancers and other disease that doctors cannot recognize in your eye. So it's quite useful. So basically what happened in practice is that the AI let doctors know that these regions might be suspicious. Then doctor take a look and double check, and then maybe take another images and so on. So they will improve their process. So this is not surprising anymore because of CHAT-GPT, but even maybe three or four years ago, I think. This is Gmail. I'm using this all the time. So if you type a little bit, then the ML automatically completes your syntax. And this is quite an interesting example because you type Taco Tuesday, and then the ML knows the contents, and then it suggests your completion of your . And Copilot, anyone have used this one when you code? It's very useful. And if you type some prompt here, just code function prototype. Then ML automatically completes the rest of the code. This is working really good. And I was told that once you start this program, then you never go back to before. But this is really convenient. And Amazon Alexa, Google Home, there are a lot of AI assistance. You can actually download it. And you can actually talk to and interact with them to do something. I think that's from our country, ,, I think. And AlphaGo, it's already seven or eight years ago. 2015 or 16, I remember. And for the first time, I'm going to use this. As a platform. I listen to what they're saying . Do you want to help me? I'm ked kind of aware if you are on Google. Can we be one-time AI assistance? Even the human expert. And this game is so complex. And the number of complications you can actually think of, people say, is it more than the number of atoms in the universe? They can tell you value. But the first step I was able to do come back to online scope and do AI. you learn from the games. And it was really surprising. And I remember at the time, the AlphaGo beat ResetDoll 4-1. And ResetDoll only won the one game. But it turns out that that was also a kind of bug. So nowadays people never beat AlphaGo. The new version of AlphaGo actually beat the old version of AlphaGo, maybe 100 to zero. The human is nowhere close to their performance. So I made this slide maybe two years ago, and I feel like all the contents are really out of date. You know what I mean? I have to update. And StarCraft, also the, it's from Google Demand, and then it beat the human professional player, and then it beat the game. So it's a little bit different than AlphaGo because the game reset, it doesn't give you the full information, right? If you look at this map, you can only see the map close to where you are. And you have to guess what's happening in the high-dip area. So it's the partially observable environment. And also, I think it's a little bit different than AlphaGo. It's a little bit different than AlphaGo. So the action space, the control space that you have to pick is really huge compared to the Go or other simple games. It's pretty impressive. And this is a robot. Trying to do Rubik's Cube. And this robot is called Shadow Hands. And they have about the same number of joints that human fingers have. And they also have some contact sensors that they can feel something that they contact with them. And here, this is really, this is only seven years ago, but when first I saw this, this dexterity is really surprising. They can, they can do this. So this is control problem from robotics. And then people have tried this control for forever. And they never imagined this is gonna happen. But it's based on the rainforest running. It's based on the simulation. And then you train your rainforest running system based on millions of trial without actually having the real robots. They apply the learned system to the real robot. And it kind of works. And self-driving car, this is obviously good example. But this is also a really hard problem. Did you guys hear the news that Apple gave up the car business? That means it's really hard. So, this is the way more from Google, Tesla and GeoPublishing, they're all good company, but it's still far from the 100% self-driving. Another example, Google has their own AI processor, which is called T-Mobile. And it's called T-Mobile. And it's called T-Mobile. And it's called T-Mobile. And it's similar to the GPU that Nvidia makes. And it's similar to the GPU that Nvidia makes. When they make their AI processor, they use AI techniques to do something. Actually, I don't know what they are trying to do, but some layout problem is really hard. But they said the AI is not much better, a little bit better than human can do in their chip designing. This is one other example. And AlphaFold, I'm gonna show the video. This is quite motivating. So, I will play this video. I play this video every course that I'm teaching. We've discovered more about the world than any other civilization before us. But we have been stuck on this one problem. How do proteins fold up? How do proteins go from a string of amino acids to a compact shape that acts as a machine and drives life? When you find out about proteins, it is very exciting. You could think of them as little biological nanomachines. They are essentially the fundamental building blocks that power everything living on this planet. If we can reliably predict proteins, if we can reliably predict proteins, if we can reliably predict the amount of 사람� Tutaj's growing speed, system alpha fold really hard now for over two years. Rather than having to do painstaking experiments, in the future biologists might be able to instead rely on AI methods to directly predict structures quickly and efficiently. Generally speaking, biologists tend to be quite sceptical of computational work and I think that scepticism is healthy and I respect it, but I feel very excited about what alpha fold can achieve. The task is when we say, look, deep mind is doing protein folding, this is how good we are on maybe it's better than everybody else, maybe it isn't. We decided to enter CASP competition because it represented the Olympics of protein folding. CASP, we started to try and speed up the solution to the protein folding problem. When we started CASP in 1994, I certainly was naive about how hard this was going to be. It was very cumbersome to do that because it took a long time. Let's see, what were we doing still to approve? Typically 100 different. Different groups from around the world participate in CASP and we take a set of 100 proteins and we ask the groups to send us what they think the structures look like. We can reach 57.9 GDT on CASP-12 ground truth. CASP has a metric on which you will be scored, which is this GDT metric. On a scale of 0 to 100, you would expect a GDT over 90 to be a solution to the problem. If we do achieve that... This has incredible medical relevance. The implications are immense. From how diseases progress, how you can discover new drugs. It's endless. I wanted to make a really simple system and the results have been surprisingly good. The team got some results with a new technique. Not only is it more accurate, but it's much faster than the old system. I think we'll substantially exceed what we're doing right now. It's a game changer, I think. In CASP-13, something. Very significant. It happened. For the first time, we saw the effective application of artificial intelligence. We've advanced the state of the art in the field, so that's fantastic, but we still have a long way to go before we've solved it. The shapes were now approximately correct for many of the proteins, but the details exactly where each atom sits, which is really what one would call a solution, were not yet there. It doesn't help if you have the tallest ladder when you're going to the moon. We hit a... A little bit of a brick wall since we won CASP. Then it was back to the drawing board and what are our new ideas. Then it's taken a little while, I would say, for them to get back to where they were, but with the new ideas. Then now I think we're seeing the benefits of the new ideas. They can go further. That's a really important moment. I've seen that moment so many times now, but I know what that means now. I know this is the time now to press. We need to double down and go as fast as possible from here. I think we've got no time to lose. So the intention is to enter CASP again. CASP is deeply stressful. There's something weird going on with the learning, because it is learning something that's correlated with GDT, but it's not calibrated. I feel slightly uncomfortable. We should be learning this, you know, in the blink of an eye. The technology advancing outside DeepMind is also doing incredible work. And there's always the possibility another team has come somewhere out left field that we don't even know about. Someone asked me, well, should we panic now? Of course. We should have been doing it. We should have been panicking before. It does seem to do better, but still doesn't do quite as well as the best model. So it looks like there's room for improvement. There's always a risk that you've missed something. And that's why blind assessments like CASP are so important to validate whether our results are real. Obviously, I'm excited to see how CASP 14 goes. My expectation is we get our heads down. We focus on the full goal to solve the whole problem. The prime minister has announced the most drastic limit. And the UK is the only country that has ever seen a virus in living memory. I must give the British people a very simple instruction. You must stay at home. We were prepared for CASP to start on April 15th because that's when it was originally scheduled to start. And it's been delayed by a month due to coronavirus. I really miss everyone. I struggled a little bit just kind of getting into a routine, especially with my wife. She came down with the virus. I mean, luckily it didn't turn out too serious. CASP started on Monday. Can I just check this diagram you've got here, John? This one where we ask ground truth. Is this one we've done badly on? We're actually quite good on this region. If you imagine that we hadn't have said it came around this way, but had put it down instead. One of the hardest proteins we've gotten in CASP thus far is a SARS-CoV-2 protein called ORF8. ORF8 is a coronavirus protein. We tried really hard to improve our prediction. Like really, really hard. Probably the most time that we have ever spent on a single target. So we're about two thirds of the way through CASP and we've gotten three answers back. We now have a ground truth for ORF8, which is one of the coronavirus proteins. And it turns out we did really well in predicting that. Amazing job, everyone, the whole team. It's been an incredible effort. Here what we saw in CASP 14 was a group delivering atomic accuracy off the bat, essentially solving what in our world is two problems. How do you look to find the right solution? And then how do you recognize you've got the right solution when you're there? All right. Are we mostly here? I'm going to read an email. I got this from John Maltz. I'll just read it. It says, John, as I expect you know, your group has performed amazingly well in CASP 14, both relative to other groups and in absolute mind. And in absolute model accuracy. Congratulations on this work. It is really outstanding. Our portfolio represents a huge leap forward that I hope will really accelerate drug discovery and help us to better understand disease. It's pretty mind blowing. You know, these results were, for me, having worked on this problem so long after many, many stops and starts and will this ever get there, suddenly this is a solution. We've solved the problem. This gives you such excitement about the way science works, about how you can never see exactly or even approximately what's going to happen next. There are always these surprises. And that really, as a scientist, is what keeps you going. What's going to be the next surprise? It's fun, right? And there is also movement. There is also movie AlphaGo, which is really, I really like it. Winter in Ukraine is always harsh. With the war continuing, it is even more difficult. This is too long. I cannot play here. It's one and a half hour. So when you're relaxed time, please take a look. It's really inspiring. Thank you. Thank you. So AI breakthrough in science and medicine and biology is really taking off right now. People try to make drugs from machine learning. They try to find new material with machine learning. So there's going to be lots of things happening in scientific discovery by using machine learning. So it's really exciting time. There are a couple of more examples that I want to show today. ChatGPT. Everyone knows. There's ChatGPT version 5 is coming soon. I was told that that's going to be a huge impact. And Dali, have you seen this before? You type some text and it generates the images. I mean, realistic images. Here the prompt is an astronaut riding a horse in a photo realistic style. And then they boom, they generate this image. Never exist in the world. Probably, right? And also picture of a horse. There's a picture of Batman in an Iron Man ship. So we don't have these images in real world, right? Because Batman and Iron Man is different thing. And they beautifully combine this concept together. This is Batman, right? And this is Iron Man. If you notice. And also another example. And. I kind of like this one. If you ever come to visit the US, there's Walmart and Target. You know, Walmart is kind of E-Mart in Korea. And Target is the same thing. But they have two. So Walmart, their signature color is blue. And then this logo is Target logo. So they kind of combine these two, which is amazing. And Taxis and Wallet. It's kind of combining these two. Interesting. The generative AI is really changing what we are trying to generate some contents nowadays. This is another example that people is doing research a lot. So you not just generate images. They generate 3D model. So if you imagine animation and VFX for movie, it's going to be really useful. So you just type some text and then it generates really high resolution 3D model. Then it can use in the other use cases. People just generate the 3D model. So this 3D model is really labor intensive. So you have to do every single mesh by your hands, which is really time consuming and annoying and also very expensive to make them some 3D model. But just give text, you can generate quite a nice 3D model. The example. The model is trying to generate this model. And then 3D is quite good. Nice. I think. And also you can do you give some images and they can generate 3D model. Which is. And I'm going to show last example that I think everyone knows right now. Have you heard about the open AI Sora? It was released last week. Or two weeks before. And then the quality is. It's this. This time. I can actually believe. So it's basically you give text and they generate video. And the quality is. Inter grasses, a focus on the editing. And the color, not a quality. So that would be theMy Otterleg's tunnel. But let me just give you one more thing. . Texture novelty. Very porous. crazy. Sorry to stop but if you see the video, then it's actually 3d consistency. It's not just generating just random stuff. They just move camera points right and then it's people never actually tried to reconstruct 3d from their video and it actually worked. That means that this underlying AI system that knows a 3d consistency and actually computer graphics rendering knowledge inside the model, which is really surprising. Now Thank you for watching. If you find this useful, like this, like subscribe my fa we do This is my favorite example Oh, do you see that? Do you see the person in the window? Yeah, here. It's really scary. Can you imagine you can make the animation, two-hour animation with the text script? I think it's possible. There are techniques that you can generate the audio as well. You can put the audio and then the video. The problem is now, generating realistic, really interesting contents, we are really close to what our audience is going to see. But now the problem is the story, how we can make a really fun and interesting story. But the play is in a way, is a littlemlah. I started my new work when child. It's like, oh, why are these comments? I always became curious. Listen, play, play with something, warm, warm, warm. Like, oh. The past. Oh. OK. Yes. OK. Yeah. So anyway, it's not obviously perfect. No possibilities. The quality is not very realistic. I think that was the first videos that were released. The people are shocked. And actually they're generating maybe five or ten videos like every two or three days. So we cannot access their system, but they said they were going to release this to some better version for some professional artists. I think we are all doomed, right? And last one, the robotics. It's not as impressive as Sora, so I'm going to just skip it. So thank you guys. See you next week. Bye. Bye. Thank you. Thank you for the interview. Thank you. Bye. Thank you. Bye.