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[Machine Learning]: Model and Cost Function Outline: 1) Linear Regression Model 2) Cost Function 3) Math Model Linear Regression Model First, we give an example of the linear regression model, the example as follows, we are given the house size and house price, we want to know the "relation" between the house price and house size. What should we do?           House Size(\(x\)):  \([2104, 2000, 1216, 1234, 852, 737, 469, 220]\);           House Price(\(y\)):  \([460, 300, 232, 315, 178, 177, 140, 100]\); T o describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function H (H is the Hypothesis Fucntion) : X → Y so that h(x) is a “good” predictor for the corresponding value of y. For historical reasons, this function h is called a hypothesis. Seen pictorially, the process is therefore like this:          -> input:   \(x_{i}\)                       -> output:   \(y_{i}\)             When

[Machine Learning]: Introduction ****************** 1.What is machine learning? The field of study that gives computers the ability to learn without being explicitly programmed.                                                                                                       - Arthur Samuel   A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E                                                                                                         - Tom Mitchell ****************** 2. Supervised Learning: 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. Supervised learning problems are categorized into " regression " and " classification " problems.  In a r