ML 101

Supervised algorithms

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.

X's

Input variables / features

M

Number of training examples

Unsupervised algorithms

This 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 We can derive this structure by clustering the data based on relationships among the variables in the data. With this learning there is no feedback based on the prediction results.

Regression problem

Trying to predict a continuous value number EXMP: Given a picture of a person, we have to predict their age on the basis of the given picture

Classification problem

discrete value problem (0 or 1). Trying to group your data into different sets. EXMP: Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

Y's

output variable / target variable