Please, Translate the following English into Korean: 
Okay, so thank you for coming to this video. This video is for the introduction to machine learning. So here let's describe and let's see the machine learning and kind of the general view. Alright, so you know, so the artificial intelligence. Actually, that's a kind of definition of the artificial intelligence is the intelligence exhibited by machine. So here the intelligence and then the machine. So that one is actually implemented in the machines. But in more detail, so actually the artificial intelligence is that how to create computers and computer software that are capable of intelligent behavior. So this kind of intelligent behavior is very important for machine learning. And then, as you know, the artificial intelligence one. So for instance, so here there is a picture, right? There is a picture and then there are two people in the picture. Then the Microsoft service actually, it's also one of the artificial intelligence to, I mean, and then it figures out the emotion from their face. So understanding and then figuring out the emotion from the face, it's actually the intelligent behaviors. So that's why that kind of service is also artificial intelligence one. And then, you know, there are several types of artificial intelligence. Then here I briefly introduce three types of artificial intelligence. The first one is artificial neural intelligence, ANI. So that's the weak AI. And then especially, they specialize in one area or one task. So for instance, the alpha-1, which is a winner of the gold game against Isadore, right? Then, you know, even if it outperforms humans, however, the alpha-1 can only play the gold game, not others. Well, we know that using some kind of data set techniques and algorithms and model something like that, then we can also build another type of the artificial intelligence model to conquer different types of tasks. However, we can only build narrow intelligence one, which means that so we build artificial intelligence model to solve and then conquer one specific area. And as you know, that's the kind of the AI speaker and then it also outperforms in question answering system. Something like that. And as you know, the spam may pitter. So that's the another artificial intelligence cause it also classified whether the incoming email is spam or not. Then to work that kind of task, then the spam may pitter should understand the contents of the text. And then, you know, the machine translator, that one is also another type of the artificial intelligence up until now. And then the next one is artificial general intelligence, AGI. So that one is actually called the strong AI or human level AI. That means that it is as smart as a human across board. So that's the main part. As you know that the ARPAGO, it's a winner of the ARPAGO game, as I said, against the human. However, that's the ANI since it cannot conquer, it cannot solve any types of the problem. But here, artificial general intelligence actually can solve many types of problems as much as human as possible. As much human as possible. And then the last one is artificial super intelligence. Well, that's a little bit easy to define the concept, right? Just smarter than the best human brains in every field. So that's why it is called super intelligence. So here there are three types of the intelligence, artificial intelligence, ANI, AGI, and ASI. Then before we jump into the artificial intelligence in more detail, then let's see the definition of the intelligence. And actually, the definition itself is that the ability to learn or understand things or to deal with new or difficult situations. So the new or difficult situation. So to solve that, I mean, that kind of things are kind of the question of the problems, right? So how to solve that kind of new and difficult problems. And that's the intelligence one. So if someone said, hey, it has intelligence, then that means that it can solve, I don't know, maybe the specific or the general problems. But anyway, so it can solve the problems. So when we say about the intelligence, then it's a kind of capacity for logic, abstract thought, understanding, self-awareness. That's also very important, you know, the self-aware, aware of the self itself. And then the communication is also one of the important things. Learning, emotional knowledge, memory, planning, creativity, problem solving. So those are highly related to intelligence. So that means that we say about the artificial intelligence, then that one should have that kind of capacity. And then one of the interesting is that when we think about the intelligence, and especially human intelligence, then you know the x-axis is time and y-axis is the kind of human progress. And then at the beginning of the time, the time means that, you know, in Earth, the human being was appeared. And then at that time, you know, survival itself is very important and then, yeah, very important kind of task. So it is hard to build some kind of human intelligence. As you know that, so here education itself is very important to grow the human intelligence. Writing a paper, writing a paper, writing books, and then remain that kind of things, and then learn from the book or papers or the lectures, then the next generation can understand and learn the previous knowledge and information very quickly. And that means that the human intelligence itself is growth. However, at the beginning of the time, you know, there is no school, there is no book, there is no character system, then it's quite low. And then based on the around then it just grows and grows and grows a little bit more. Well, that's because so we have some kind of ways to study and then to do the research, something like that. All right. Anyway, then, you know, one of the interesting patterns of this human intelligence growth is that it's kind of S-CUB. You know, the S-CUB means that even time is going, there is no big difference between two times. However, one kind of event was happening, then it grows dramatically. Well, it's kind of a paradigm change, right? And then one of the examples of this one is deep learning. Yeah, I guess, sorry, I think you already heard about this one. So deep learning. So deep learning is actually an artificial neural network based model. However, we can analyze the models and then instead the capacity of the model itself is also grows and then we can solve many types of the problems by these deep learning models. Then actually that one is appeared about 2007 or something like that. And then after that, you know, only 10 years or 15 years later, now AI is much better than before. Well, as you know, I'm an NFP researcher, so one of the biggest changes between 2007 and now is machine translation area. You know, the empty part, the machine translation. And then based on this deep learning, the performance of the machine translation is dramatically increased. I don't know exactly you know the performance of the machine translation at that time, about 15 years ago. So actually it is hard to use. However, now it's actually much better than before. And then we can easily, not kind of, kind of easily to understand the other languages sentences. For example, English or the little Korean and English and German and something like that. So what I wanted to say in this slide is that the human intelligence growth looks like the S curve. So there are some kind of big events and then dramatically increase the human intelligence. And then here, what is interesting is that, you know, from 19 and then it grows, right? And then it's actually kind of the performance of the chipset. And here, at the beginning of time, it's the worse than the one insect brain. And then it grows and grows and grows and grows and grows. And then at the 2000 now, it leads to the one insect brain, I mean the artificial intelligence one. But it also increased and increased and increased. And then here, the author said and insisted that at 2015 or 2016, then this computing power leads to the old human brains. But anyway, so here, as you know, at that time, the AI itself is appeared and then it leads to the anti-level. And still, the Einstein level is much higher than the others such as the chimpanzee, birds or the ants, something like that. And then one of the interesting thing is that the growth of the AI is too high and too speedy rather than the before. So one of the interesting things, as I said, is that the growth of the human intelligence and the artificial intelligence itself is very speedy. And then someone insisted that at 2014, sorry, in 2014, the median expected prediction for AGI. And then in 2016, so we can also see the HSI, which is the artificial super intelligence one. Wow, you know, now in 2020, we can see that maybe 20 years later or 40 years later, we might see the kind of the artificial general one and the artificial super intelligence one. So then we can, I mean, the people just think about the kind of opinions about the ASI. So when ASI is arrived, then what kind of things are happen? And then here, so here, when ASI arrives, will it be good or bad for us? So us means that you know the human, right? And then the another one is that when will the ASI arrive? Maybe definitely soon or definitely not soon, something like that. And here you know that it's a bad one to good one. So based on the answers for this question, so we can say that the left part is pessimism and then the right part is optimism. All right. Then we might see that the right upper corner is confidence corner, which is the optimism part. And then the left one is ancestor avenue, something like that. But anyway, so let's think about the ASI arrives one. Then, as I said, there are two kind of two types of things. The first one is optimism. Then based on the optimism mind, then they said that AI can solve any problems. Why? Because the super intelligence one. And then as we expect that the AI is much smarter than the human one. So AI can solve any types of problems instead of human. And then one of the things is one of the problems maybe is the eternal life. So people want to have eternal life, then maybe, I don't know, maybe the AI can scan the brain of a human and then save that kind of the information or kind of the brain one. I mean, yeah, the brain one. I mean, AI can digitalize the brain information and then maybe they can save and then copy and then implement to another body, something like that. And that means that it's like eternal life. Right? I don't know, maybe. As you know, that is, as I said, it's a kind of the thinking about the when ASI is coming. But here, another approach or aspect is the pessimism one. One of the things is that, you know, actually, AI work hard to achieve the goal. So the goal means that, well, actually, if you learn about machine learning, then you might heard about this terminology. What's that? The objective function. OR loss function. OR cost function. So to run the AI model, then we need to maximize this objective function or minimize loss or cost function. So that means that there is some kind of the goal to run the AI. And AI, as I said, AI works hard to achieve the goal and maximize the objective function or minimize the loss function, something like that. So one of the interesting thing is that AI are amoral. So the amoral means that it's not moral and it's not immoral one. That's interesting point, isn't it? So this kind of AI is, you know, asymmetric, something like that. I'm sorry. Just you know the part. But anyway, it's a little bit hard to understand it. But anyway, so the definition of the amoral is that, as I said, not moral and not immoral. Just not involving questions of right or wrong. So you know, right or wrong or good or bad or something like that. You know, that's very important to make a decision from human. Actually getting objective function is self, sorry, getting a high objective or getting high rewards or getting low risk. That kind of thing is very important. However, when we think about the decision, then morality is itself is also very important criteria or the features. But unfortunately, up until now and then maybe in the future, AIs are amoral. So AIs do not care about this kind of morality. Well, you know, that's actually related to the problem. So there is the train in here and then there are two types of the way and then the body, some humans are on here. Then can we switch to the next one? Can we change, sorry, can we switch the direction of the train? And then that's a kind of a dilemma. How can we solve a problem? Something like that. However, maybe AI doesn't care about that kind of morality. Just maybe achieve, just work as, just work to achieve maybe high reward or low risk, something like that. Yeah, so that's the, that's why so we, sorry, that's why, that's why, sorry. So that's why, so the AI, so I mean, that's why, so when AIs are coming, then AI work like a machine. That means that, yeah. So even AIs have a better smart power than humans, but they might not have this kind of morality. Yeah, so that's the, another opinion about the ASI. But anyway, so as I, as you know, that is a kind of the future part. So we don't know exactly. So that kind of thing is happening or not. Oh yeah. And then here, one more thing. Can humans control ASI? Where control? I don't know. But however, so when we think about ASI as a tool, then control itself is very important. But ASI, you know, the definition of ASI is that ASI is smarter than humans, then can we possibly, can we possible, can we be possible to do that? I don't know. Yeah. So in the future, so here, one of the interesting is that it's the worst part and then the better part. So there are two likely possible states for the human race after ASR. So that's one of the interesting points. And then we need to think about the next one. All right. And then, but anyway, so until now, I will briefly introduce the types of the artificial intelligence. And now I briefly also introduce the, introduce machine learning. So actually, you know, the machine learning is a subfield of artificial intelligence. So machine learning, you know, the ML is not equal, is not the same as the artificial intelligence. Actually, the machine learning is one of the types of the AI. So here, the definition of the ML is that study of pattern recognition and computational learning theory. And then we can create a program that can automatically learn rules from data sets. So that's very important thing. So that's why in 1959, so I thought somewhere, said that it's a field of study that gives computers the ability to learn without being explicitly programmed. Yeah. So that's the very important concept to describe what the machine learning. Then what's the meaning of this one? So here, there are two kinds of the AI program. So the goal of the AI program is to figure out and identify whether this image has cat or tiger. You know, it's a cat and then tiger, right? Then this kind of program, once, so we wanted to make a program to identify the animals in the portal. Then one of the way is that, you know, we know some kind of features about the cat and then the tiger. So we can write the program or something like that. If something and the early for something early for something. And then based on these kinds of rules, then that program can distinguish between cat and tiger. Yeah. That's the traditional one. However, when we do the machine learning, that means that we use actually kind of the training data. The training data means that there is a label for each image. So this one is a cat and this one is a tiger. And then based on this training data, machine learning algorithm model can learn and then figure out the rules between cat and tiger based on the training data set. Yeah. So that's the main difference between the machine learning and then it's kind of the complement of the machine learning. And then nowadays machine learning outperforms non machine learning part. So that's a traditional one in terms of generality. That means that if we made a new image or new test data set, then machine learning model outperforms rule based one. So that's why nowadays, actually, the people said that the machine learning is equal to the AI. However, strictly speaking, so machine learning is a subfield of artificial intelligence. All right. And then as you know, there are two big types of the types in machine learning. So the first one is a supervised learning. So it's a predictive approach. And then it learns a mapping from input to output. So the example of the supervised learning is class, examples of the supervised learning are classification and regression. And then the another one is unsupervised learning. So it's a descriptive approach. It finds interesting patterns in the data set. So the interesting pattern, that's the important part. So the unsupervised learning, it figures out interesting patterns and then the supervised learning, it just runs mapping kind of function from input to output. So that's why the examples of unsupervised learning are clustering. You know, based on the clusters, then we can easily introduce and then figure out the patterns of the data set. And then the dimensional reduction, that's one of the ways to see the interesting or hidden patterns. So when we project into two dimensional spaces, then we can see the patterns in two dimensional areas. Maybe the display or the paper, something like that. Yeah. All right. Then now I briefly introduce the supervised learning and unsupervised learning. So as you know, in the supervised learning area, the types of the problem, there is a training data and then it has labels. So here there are how many? N data sets and each data has their own label. So here Y is a label. And then based on this one, the goal of the supervised learning is running a rule F that takes X and then the output is Y. Then based on this rule, we wanted to figure out the output Y for new inputs X. So this one is very important. New one, not existing one. So if there is a new input X, then based on the run rule F, then we can figure out the kind of the label or kind of the output of Y for new inputs X. So let's see the example. Let's see an example. So there are several shapes in the figure. So maybe the skull, the lips and then the soccer or the star, something like that. Now what's interesting is that those are labeled as yes and those are labeled as no. Then based on this one, we can say the data set like that. So blue skull and the size is 10, then label is yes. Red ellipse and then the size is 20.7, then we can say that that one is no label. So maybe this one, right? And here the task is that for new inputs, blue class 10, maybe this one, right? And then are they, is this, is it yes or no? As you know that this is a new X, right? Then based on this new X, let's predict the label or let's predict the output. Yes or no. Something like that. So that's why it's a supervised learning one. All right. And then there are two types of the supervised learning. So the first one is regression and then the next one is a classification. So the main difference between regression and then classification is that regression is real valued outputs. So the output itself is a real value, you know, some kind of numbers. However, in classification, the output itself is discrete one. So the discrete one means that, you know, in this example, so we wanted to figure out whether the animal is a cat or a dog based on the size information, such as age and the weight. So here, cat and dog, it's not a number. So it's a kind of discrete one. So that's why that kind of thing is called classification. But when the output itself is a real number, then we can say that this one is regression one. So we will see the difference and then difference between them. And then we will also see each part in the layer. All right. And then the next one is, as you know, that is unsupervised learning. So unlike supervised learning, there is no labels in a data set. Just X. Right. There's no Y. But from this one, we wanted to figure out the latent structure that summarize and explain the data set. So here, let's see our example. So the data set itself is items purchased by customers at the supermarket. So that means that each column represents the items, coffee, tea, milk, beer, diet for us, something like that. And then each row represents the customer. So customer number one, he or she bought coffee and milk. And customer number two, he or she bought aspirin only. And then customer number three, he or she bought tea and milk. Tea, milk, diapers and aspirin. Wow, there's a lot. Anyway, so that's a kind of the data set. And there is no label actually. But from this data set, we wanted to figure out some kind of interesting patterns. Well, here there is one example of the question. Do customers buy tea when they buy coffee? So let's imagine that someone buys coffee, then can we say that he or she also buys milk and buys tea? You know, well, actually, one of the ways to solve that question is asking his or her mind. However, you know, it is hard. That's why from the customer's behaviors, we wanted to figure out the hidden structure, which is human's mind. To understand and to figure out that kind of hidden one, we needed to do the unsupervised learning part. All right. And then there are actually, as you know, there are several types of unsupervised learning. But here I will briefly into this one. The first one is clustering. So clustering is actually finding hidden patterns, but that hidden structure itself is homogeneous groups. The homogeneous group itself is a latent structure. That's the assumption of the clustering part. But in the dimensionality reduction, there is another way. So we wanted to figure out the low dimensional latent structure and especially two dimensional one. Why? That's because, you know, humans can see the two dimensional one and three dimensional one only. Well, actually, so there are four or five or six dimensional ones. However, it is hard to figure out the patterns since, you know, the space itself is three dimensional case. But anyway, so as I said, there are two kinds of tasks in unsupervised learning. The first one is clustering and the second one is the dimensionality reduction. So I will also describe and explain the details in the later. So now just see what is it and then please expect the next videos to understand all kinds of things. All right. And then here, one of the interesting is that there are several learning paradigms in machine learning area. So the first one is reinforcement learning. So reinforcement learning is learning how to act when given occasional rewards or the punishment in an environment. So the setting itself is quite interesting. So here there is an agent and then agent itself is in environment. And then the agent itself acts something where there is some kind of action. And then based on this action, the environment returns reward and then the next state. And then based on these two things, then agent also do the next action and then getting the reward and the state once more and once more and once more. And then here the objective of this learning paradigm is that how to get a maximized reward. So how to maximize this reward. So that's the main goal of this setting. So another interesting, one of the examples of this reinforcement learning is that you know the robot per tumor. So for example, it's a room and there is a little per tumor or a little cleaner. Then if that one, I mean the action of this robot one is that up, down, left and right. And then you know it's a dirty space at the beginning of the time. And we wanted to move these areas to clean the room. Then you know, as I said, action is up, down, left and right. Then if that one moves to up, then it gives some kind of reward. Because here at the beginning of the time, it's a dirty space. And then the state itself is the location of this part. And the next action is maybe the up or down and the left or right. But now the action itself is right, then you know the reward might be general or negative. Because it cannot move through this world. So and then the state itself is the same. So that kind of thing is called reinforcement learning and then it's also very interesting one. Unfortunately, this lecture is just mathematics for machine learning. So I will also briefly introduce the reinforcement learning and then relate the mathematics ones for you in this course. But that's it. Anyway, so I wanted to introduce about it. And then the next one is online learning. Yeah, so that's also very interesting paradigm. Where the online learning, so what's online learning? Well, it's a learning with one example or a small mini batch of examples at a time. So at a time means that, you know, sometimes, not sometimes, for example, let's think about this one. Well, actually, here the task itself is some kind of, where is it? Yeah, you know, the class as you in the previous time. So classify whether it's a cat or dog. Right. Then we have maybe 10K training data. Then using this one, we can train the model, the machine learning model, right? The machine learning model. And then there is some kind of maybe 100 test data. Yeah. And then we can see the performance of learned machine learning model, right? Learned, sorry, learned machine learning model based on this test data. However, maybe, maybe, you know, maybe the performance is quite low. Then let's make additional training data, maybe the 1K additional one training data. Then how can we train this machine learning model? One of the ways is that merging these two training data, right? Then it's 11K one. And then let's run the training data from scratchy. That's one of the way. But you can see, you can also already know that, you know, it's very inefficient way. Since we already use this 10K training data to run the machine learning model, why do we use this one as additional one? I mean, kind of additional running one based on this learning, learned machine learning model, right? So to do that kind of the approach, that's the online learning part. So the online learning is important algorithm or the settings to make that kind of this scenario. So one of the interesting is that the deep learning or the artificial neural network one is online learning based one. So that's why you don't need to worry about it. However, if you see the another types of the machine learning models, such as Bayesian network or other kinds of things, sometimes it has their own online learning algorithm. However, sometimes it's not. So please check how can we use the online learning algorithms for your own machine learning models. And the next paradigm itself is transfer learning or it is called meta learning or learning to run. So it is actually leveraging knowledge of solving one problem to to solve a new problem. You know, and actually human already do that kind of things. For instance, when you learn about the C++ language, how to write a C++ source code, I'm sorry, how to write a source code over C++, then based on this knowledge, you can easily understand and learn how to write Java code. Why? Because there are some kind of common things, writing programming code. However, it is where but when you think about this one, so we wanted to write a Java programming code, but if we do not know any types of the programming source or the language, such as C++ Java or C++ Python or C, something like that, then it means that learning itself is from scratch. Then it takes a long time. Compared with the online learning, online learning is just a data set. However, here it's actually used already trained knowledge. So that's a little bit different one. So and then that's why the transfer learning itself is also a very interesting topic in machine learning area. And then the next one is, sorry, the next. And then the next one is active learning, or it is called human in the loop. So it's a learning algorithm can actively query the users for labor. What does that mean? So here one of the interesting is that, so let's see this one. Yeah, the same thing, the classify whether this one is a dog or a cat, and then maybe based on the image or the portal. Then maybe there is, then you know, the AI classifier runs the patterns between them from the training data. But sometimes AI classifier has some kind of limitation or the hard things. Then that means that maybe for example, this one, this is one of the image and then it is harder to figure out this one is a cat or dog. You know, because of the ambiguity, sometimes, you know, it is harder to solve that kind of problems. However, human can solve this problem a little bit easier. Right. So the main problem of the human resources is just it's very cost ineffective one. However, human can label the data set. Right. So that's why AI, run AI use the training data to train themselves. However, when he meets the ambiguity, then AI can ask whether, can ask to human to overcome that kind of ambiguity. So that's why it is called human in the loop. That's why it is called active learning. So unlike the others part, so unlike the others, sorry, if you do not use the active learning, that means that, for example, as I said, so there is a 10K training data set. Then the performance is not good. So that's why we need to bring how many? 1K training one. Right. As you know, it's also still large training data set. However, when you use the active learning, then the AI model said that, ah, this kind of the data set has some kind of ambiguity. So please re-label or the label is self-revealing this kind of data set. Then human can see this one and then the number of this one is much less than the 1K one. Right. Maybe for example, 10 item, 10 data set or the 100 data set. But as I said, these are the very ambiguous data points. So from the human label, additional human labeling one, then it is AI can improve their performance dramatically rather than using just more data set. Yeah. So that's why it's also another learning paradigm in machine learning area. So there are several, as I said, the other, there are several learning paradigms, which I do not also introduce. So if you wanted to know that kind of things, then please read the recent machine learning papers. So another interesting is that the machine learning. So if you wanted to know about the recent trend of the machine learning and the recent algorithms of the models of the machine learning. So you need to read good papers. Then usually that kind of the good papers are appeared in top tier conferences. So for instance, NeurIPS, AAAI, ICMA, KDD, WWW, EZ-CAL, ICLR. Yeah. So those are the general parts of the AI and machine learning conference. And if you are interested in computer vision, then CBLI, PI, ICCB, ECCB, yeah, those are good conferences for computer vision. And then if you are interested in natural language processing, then HGA, MNIP, NIHGA. So those are also good conferences in much NIP area. So if you wanted to know about the top tier conferences in machine learning, then you can also see the lists in this page. So here you are a graduate student. So if you wanted to get more recent and then good information, then I believe that course, textbook or book, that's good for your basic one. However, if you wanted to know about, as I said, recent one, then reading a paper itself is very important rather than taking the course. All right. But anyway, you know, so it's a mathematics for machine learning course. So as you know that there is a kind of the pillars of machine learning. So regression, dance estimation, classification and other kinds of things. And then in the bottom part, vector calculus, probability distribution, optimization, linear algebra, analytic geometry, matrix decomposition. Yeah, those are the basic basis of the machine learning. So in this course, I will also introduce that kind of the basic one to understand machine learning models and algorithms. So let's do that. All right. So thanks. That's it. So anyway, so thank you for listening to this video and then we will see that.