# Types of Machine Learning problems: Supervised, Unsupervised, and Reinforcement Learning

## Introduction <a href="#introduction" id="introduction"></a>

## **Supervised Learning** <a href="#supervised-learning" id="supervised-learning"></a>

* **we have a set of features or inputs X (for example, an image) and our model will predict a target or output variable y**
* **Types:**
  * **Classification (e.g. spam/not spam), Regression (e.g. housing prices)**

## **Unsupervised Learning** <a href="#unsupervised-learning" id="unsupervised-learning"></a>

* **Identify patterns in the data**
* **Types:**
  * **Clustering: inherent groupings in the input**
  * **Association: discover rules that describe portions of the input data**

## **Reinforcement Learning** <a href="#reinforcement-learning" id="reinforcement-learning"></a>

* **the input itself depends on actions we take**
* **robot is known as an agent, and is in some environment (surrounding). At each time step, it can take some action and it might receive some reward.**

## **Hybrid Models** <a href="#hybrid-models" id="hybrid-models"></a>

* **Semi-supervised learning: subset of data is not labelled (e.g. image recognition)**
* **Transfer Learning: transferring our knowledge/model from one domain to another**
  * **E.g. learn a model on the alphabets, and re-use the model in some way when learning a model on the sentences.**

**Note: A majority of the successful machine learning products currently fall under the category of supervised learning.**

## Important Note <a href="#important-note" id="important-note"></a>

Unsupervised and reinforcement learning are areas of active research, and we've recently made significant progress in both with algorithms such as Generative Adversarial Networks (for unsupervised learning) and Deep Q-networks (for reinforcement learning).<br>