**Supervised learning terminology/notation**

is the dataset used to train the model*Training set*- $x$ - the input variable, the input feature
- $y$ - the output variable, the target variable
- $m$ - the total number of the training examples (training data points)
- $(x^{(i)}, y^{(i)})$ - the $i$-th training example (for $i=1,2,\dots, m$); here the expression $(i)$ is just a superscript, it does not denote exponentiation

- $f$ - hypothesis function, prediction function; also $f$ is called the model
- $\widehat{y} = f(x)$, where $x$ is the input variable and $\widehat{y}$ is the prediction for the target variable $y$
- $\widehat{y}^{(i)} = f(x^{(i)})$, where $x^{(i)}$ is the $i$-th training example, and $\widehat{y}^{(i)}$ is the prediction value corresponding to $x^{(i)}$
*Cost function*

**Linear regression with one variable (univariate linear regression)**

When we have only one input variable we call this model/algorithm

**. In a univariate linear regression model, we have $f(x) = wx + b$, where $x$ is the input variable, and $w, b$ are numbers which are called***univariate linear regression**. So $f(x)$ is a linear function of $x$. It's also written sometimes as $f_{w,\ b}(x) = wx + b$. By varying $w$ and $b$ we get different linear models. The parameter $w$ is called the***parameters of the model****slope**, and $b$ is called the*y-intercept*because $b=f(0)$ and so $b$ is the point where the graph of $f(x)$ intercepts the $y$ axis.When we have more than one input variable (more than one feature) the model is called

*. In this case we try to predict the values of the target variable $y$ based on several input variables $x_1, x_2, \dots, x_k$. Here we have $k \in \mathbb{N}, k \ge 2$.***multivariate linear regression**In a

*model the most commonly used cost function is the***univariate linear regression***cost function.***mean squared error**$$J(w,b) = \frac{1}{m} \cdot \sum_{i=1}^m (\widehat{y}^{(i)} - y^{(i)})^2$$

In the last formula

$\widehat{y}^{(i)} = f_{w,b}(x^{(i)}) = wx^{(i)} + b$

for $i=1,2,\dots,m$.

So for the cost function we also get the following expression

$$J(w,b) = \frac{1}{m} \cdot \sum_{i=1}^m (f_{w,b}(x^{(i)}) - y^{(i)})^2$$

Note that $J(w,b) \ge 0$

In practice an additional division by 2 is performed in the formulas given above. This is done just for practical reasons, to make further computations simpler. In this way we get the final version of our cost function.

$$J(w,b) = \frac{1}{2m} \cdot \sum_{i=1}^m (\widehat{y}^{(i)} - y^{(i)})^2$$

$$J(w,b) = \frac{1}{2m} \cdot \sum_{i=1}^m (f_{w,b}(x^{(i)}) - y^{(i)})^2$$

Then the task is to find values of $w,b$ such that the value of $J(w,b)$ is as small as possible.

The smaller the value of $J(w,b)$, the better the model.

All this info about univariate linear regression can be summarized with the following picture

(pictures credits go to Andrew Ng's Coursera ML Course).