Supervised Learning & Unsupervised Learning

 Coursera lecture summary 

Supervised Learning & Unsupervised Learning

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 reltionship betwwen the input and the output.

 

Supervised learning problems ar categorized into "regression" and "Classification" problems.

In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.  In a classification problem, we are insted trying to predict results in a discrete(이산) output. In otehr words, we are trying to map input variables into discrete categories.

 

Unsupervised Learning 

Unsupervised learning allows us to approacch problems with little or no idea what our results should look like. We can derive(도출, 유도) strcture from data where we dont 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 unsupervised leaning there is no feedback based oon the prediction  results.