Answers.Body: <p>Things in <em>italics</em> should give you enough googleable terms to start a deeper dive :P.</p>
  <p>There are 3 main branches of statistical ML.</p>
  <ol>
  <li><p><strong>Supervised Learning</strong>
  This approach is taken when a problem can be phrased as associating some <span class="math-container">$X$</span> with some <span class="math-container">$Y$</span>. For example, <em>classifying</em> a picture of a cat (<span class="math-container">$X$</span>) with the label “Cat” (<span class="math-container">$Y$</span>). Training in supervised learning usually means presenting some <span class="math-container">$X$</span>, having an agent predict the label, comparing the prediction with the answer to get an error metric, and finally using the error to update the agent to make better predictions on future <span class="math-container">$X$</span>s. <em>MNIST</em> is a great example of a classification domain.</p>
  <p>1.1 Supervised learning also works with <em>regression</em> when we want to learn an associated number instead of a label, such as the risk of heart disease given years of smoking and weight.</p>
  </li>
  <li><p><strong>Unsupervised Learning</strong>
  This type of learning is used when we want to draw inferences from a dataset without labeled responses. It can be used to find hidden patterns or grouping in data.</p>
  </li>
  <li><p><strong>Reinforcement Learning</strong>
  The <span class="math-container">$X$</span>s and <span class="math-container">$Y$</span>s in supervised learning are <em>i.i.d.</em>, meaning that they don’t relate to the previous <span class="math-container">$X$</span> that a model has previously observed. However, there are many problems where the most recent <span class="math-container">$X$</span>, <span class="math-container">$X$</span> at time <span class="math-container">$t$</span> (<span class="math-container">$X_t$</span>), is very important to figuring out what <span class="math-container">$Y$</span> to predict (<span class="math-container">$Y_{t+1}$</span>) for the next <span class="math-container">$X$</span> (<span class="math-container">$X_{t+1}$</span>). The field of reinforcement learning (RL) builds on this notion by framing observations as data that is generated by an agent interacting with an environment. Specifically, the field of RL poses the problem of finding a behaviour (a policy) to select actions such that an agent using the policy can maximize a special signal called the <em>reward</em> from the environment. The <em>agent</em> starts in a <em>state</em> of the world (<span class="math-container">$S_t$</span>) at time <span class="math-container">$t$</span> and takes some <em>action</em> (<span class="math-container">$A_t$</span>) which puts it in a new state (<span class="math-container">$S_{t+1}$</span>) and gives it a reward (<span class="math-container">$R_{t+1}$</span>).</p>
  </li>
  </ol>
  <p>Neural networks are a technique for finding features (some kinda notable differences) in data. They can be used in all of the above learning strategies but are not necessary for them although their application has had some very powerful results recently. Deep ___ learning is simply the application of a multilayered neural network on one of the above learning methods.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2017-12-11T04:28:55.030Z
Answers.LastActivityDate: 2020-12-16T22:02:26.573Z
Answers.LastEditDate: 2020-12-16T22:02:26.573Z
Answers.UserDisplayName: Jaden Travnik
Body: <p>There seem to be so many sub-fields, so I'm interested in getting a better understanding of the approaches.</p>
  <p>I'm looking for information on a single framework per answer, in order to allow for granularity without the overall answer getting too long. For instance; Deep Learning Neural Networks would be a single answer.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-16T18:04:37.377Z
Comments.Text: [Here](https://ai.stackexchange.com/q/12558/2444) is a related question.
Comments.UserDisplayName: nbro
Tags: <machine-learning><reinforcement-learning><unsupervised-learning><supervised-learning><taxonomy>
Title: What are the different approaches used in Machine Learning?
UserDisplayName: kakaz
ThreadText: # What are the different approaches used in Machine Learning?
  
  (Asked by: username_0 on 2017-12-10)
  
  There seem to be so many sub-fields, so I'm interested in getting a better understanding of the approaches.
  
  I'm looking for information on a single framework per answer, in order to allow for granularity without the overall answer getting too long. For instance; Deep Learning Neural Networks would be a single answer.
  
  **Tags:** machine-learning, reinforcement-learning, unsupervised-learning, supervised-learning, taxonomy
  > **Comments:**
  > **username_1**: [Here](https://ai.stackexchange.com/q/12558/2444) is a related question.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-12-11. Score: 14 | Accepted answer
  
  Things in *italics* should give you enough googleable terms to start a deeper dive :P.
  
  There are 3 main branches of statistical ML.
  
  1. **Supervised Learning**
     This approach is taken when a problem can be phrased as associating some $X$ with some $Y$. For example, *classifying* a picture of a cat ($X$) with the label “Cat” ($Y$). Training in supervised learning usually means presenting some $X$, having an agent predict the label, comparing the prediction with the answer to get an error metric, and finally using the error to update the agent to make better predictions on future $X$s. *MNIST* is a great example of a classification domain.
  
     1.1 Supervised learning also works with *regression* when we want to learn an associated number instead of a label, such as the risk of heart disease given years of smoking and weight.
  2. **Unsupervised Learning**
     This type of learning is used when we want to draw inferences from a dataset without labeled responses. It can be used to find hidden patterns or grouping in data.
  3. **Reinforcement Learning**
     The $X$s and $Y$s in supervised learning are *i.i.d.*, meaning that they don’t relate to the previous $X$ that a model has previously observed. However, there are many problems where the most recent $X$, $X$ at time $t$ ($X\_t$), is very important to figuring out what $Y$ to predict ($Y\_{t+1}$) for the next $X$ ($X\_{t+1}$). The field of reinforcement learning (RL) builds on this notion by framing observations as data that is generated by an agent interacting with an environment. Specifically, the field of RL poses the problem of finding a behaviour (a policy) to select actions such that an agent using the policy can maximize a special signal called the *reward* from the environment. The *agent* starts in a *state* of the world ($S\_t$) at time $t$ and takes some *action* ($A\_t$) which puts it in a new state ($S\_{t+1}$) and gives it a reward ($R\_{t+1}$).
  
  Neural networks are a technique for finding features (some kinda notable differences) in data. They can be used in all of the above learning strategies but are not necessary for them although their application has had some very powerful results recently. Deep \_\_\_ learning is simply the application of a multilayered neural network on one of the above learning methods.
  
  
  ---
  
  