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tags: machine-learning
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# Supervised vs Unsupervised

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<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/blog-machine-learning/supervised-vs-unsupervised/1.png" height="100%" width="70%">
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> This is my personal notes taken for the course [Machine learning](https://www.coursera.org/learn/machine-learning#syllabus) by Standford. Feel free to check the [assignments](https://github.com/3outeille/Coursera-Labs).
> Also, if you want to read my other notes, feel free to check them at my [blog](https://ferdinandmom.engineer/machine-learning/).

# I) Supervised learning

It itself categorized into 2 problems:

1. Regression problem.
2. Classification problem.

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 instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.

<ins>Example</ins>:

- **Regression**: Given a picture of a person, we have to predict their age on the basis of the given picture.
- **Classification**: Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

# II) Unsupervised learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like.

We can derive this structure by clustering the data based on relationships among the variables in the data.

<ins>Example</ins>:

**Clustering**: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.

**Non-clustering**: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).