Why perceptron is linear classifier? It is said that perceptron is linear classifier, but it has a non-linear activation function f = 1 if wx - b >= 0 and f = 0 otherwise
If i will use some non-linear function on linear combination of my data, i think i will get a non-linear classifier. Why it is false?
 A: It is called a linear classifier because its decision boundary is given by a (linear) hyperplane. Such a hyperplane is given by the set $\{ x | w^tx =b \}$ which thus splits $\mathbf R^n$ into two classes, $\{x | w^tx \leq b\}$ and $\{ x | w^tx > b\}$. You can think of $w$ as the normal vector to this hyperplane and $b$ as an offset by which you shift the hyperplane. 
A: The activation function of a perceptron is linear in it's parameters, i.e. the decision boundary is defined by a linear function of the form $w^tx = b$ = $\sum_{i} w_{i} * x_{i}$. 
Similar to linear least squares, the perceptron does not care whether the input $x_{i}$'s are non-linear combinations of your original data. You can indeed model a non-linear decision boundary using a polynomial basis function, e.g. if your data consist of a single independent variable $x$ you could include the square of $x$ as an additional variable. 
Again, the perceptron can only model a linear decision boundary, but linear decision boundaries in quadratic space become a non-linear curve when mapped back to the original space. 
