찌르렁의 IT

Let's see a case of regression problem. We can make a straight line of hypothesis($h_\theta(x)$) in this case and it depends on the value of $\theta_0, \theta_1$. Below picture shows cost value which dependes on the hypothesis. Using this graph, we can make a contour plot. A contour plot is a graph that contains many contour lines. A contour line of a two variable function has a constant value a..

Machine Learning 2020. 10. 26. 19:32

Let's see a training set shown below: In this training set, we can make hypothesis function like this: $$h_\theta = \theta_0 + \theta_1x$$ The $\theta_i$ is parameters. Below graphs show shapes depends on the $\theta_i$: We can measure the accuracy of our hypothesis function by using a cost function. This takes an average difference (actually a fancier version of an average) of all the results o..

Machine Learning 2020. 10. 26. 18:12

Let's see an example: To establish notation for future use, we'll use $x^{(i)}$ to denote the "input" variables (living area in this example), also called inpute features, and $y^{(i)}$ to denote the "output" or target variable that we are trying to predict (price). A pair ($x^{(i)}$, $y^{(i)}$) is called a training example, and the dataset that we'll be using to learn '''a list of m tarining ex..

Machine Learning 2020. 10. 26. 17:32

Unsupervised Learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables. In supervised learning, we know what output is (X is A, O is B). But, in unsupervised learning, we don't know about the outputs. we just have the data of $x_1$, $x_2$. We can derive this struc..

Machine Learning 2020. 10. 26. 16:42

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Let's see an example about Housing price prediction: This data is prices of house sold by house size. This values are continuous. So we can use data to create a linear function or quadratic function etc, for pred..

Machine Learning 2020. 10. 26. 16:10

Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn withoute being explicitly programmed. "This is an older, informal definition. ​ Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with repect to some class of tasks T and performance measure P. If its pe..

Machine Learning 2020. 10. 26. 14:51