Answers.Body: <p>I disagree with the context that MNIST is the &quot;hello world&quot; of supervised learning. It is definitely, though, the &quot;hello world&quot; of <strong>image classification</strong>, which is a very specific sub-field of supervised learning.</p>
  <p>I'd consider the <a href="https://archive.ics.uci.edu/ml/datasets/iris" rel="noreferrer">Iris</a> dataset a better candidate for the &quot;hello world&quot; of supervised learning, with other close candidates such as the <a href="https://archive.ics.uci.edu/ml/datasets/Wine" rel="noreferrer">Wine</a>, <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" rel="noreferrer">Wisconsin breast cancer</a> or <a href="https://www.kaggle.com/uciml/pima-indians-diabetes-database" rel="noreferrer">Pima indians</a> datasets. However, as an even simpler and more alternative choice, a lot of people prefer generating <a href="https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html" rel="noreferrer">their own 2-dimensional datasets</a> so that can more intuitively understand what the different algorithms are doing. An example of this is <a href="https://playground.tensorflow.org" rel="noreferrer">TensorFlow playground</a>.</p>
  <p>Equivalently, in unsupervised learning there are a lot of different tasks. I personally think that <strong>clustering</strong> is probably the task that is easier for people to understand and as such the most common intro to unsupervised learning. Here there are, as well, two options:</p>
  <ul>
  <li>Using an already established dataset, e.g. <a href="https://archive.ics.uci.edu/ml/datasets/iris" rel="noreferrer">Iris</a> (without the labels).</li>
  <li>Generating your own synthetic 2-dimensional data, to better understand how the algorithms work. An example is <a href="https://github.com/djib2011/python_ml_tutorial/blob/master/notebooks/26_clustering.ipynb" rel="noreferrer">this</a>.</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-14T09:48:56.537Z
Answers.LastActivityDate: 2020-09-14T09:48:56.537Z
Answers.UserDisplayName: Djib2011
Body: <p>As a followup to <a href="https://ai.stackexchange.com/questions/23561/what-is-the-hello-world-problem-of-reinforcement-learning">this question</a>, I'm interested in what the typical &quot;Hello World&quot; problem (first easy example problem) is for unsupervised learning.</p>
  <p>A quick Google search didn't find any obvious answers for me.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-14T06:32:41.943Z
Comments.Text: Probably knmeans clustering or gaussian clustering
Comments.UserDisplayName: FourierFlux
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-14T06:45:42.550Z
Comments.Text: @FourierFlux Both seem to be solution methods, not problems. Do I understand correctly that problems would be: cluster $n$ training observations in a $d$-dimentsional space, then apply those clusters to new observations?
Comments.UserDisplayName: Christian Aichinger
Tags: <unsupervised-learning>
Title: What is the “Hello World” problem of Unsupervised Learning?
UserDisplayName: Christian Aichinger
ThreadText: # What is the “Hello World” problem of Unsupervised Learning?
  
  (Asked by: username_0 on 2020-09-14)
  
  As a followup to [this question](https://ai.stackexchange.com/questions/23561/what-is-the-hello-world-problem-of-reinforcement-learning), I'm interested in what the typical "Hello World" problem (first easy example problem) is for unsupervised learning.
  
  A quick Google search didn't find any obvious answers for me.
  
  **Tags:** unsupervised-learning
  > **Comments:**
  > **username_1**: Probably knmeans clustering or gaussian clustering  
  > **username_0**: @FourierFlux Both seem to be solution methods, not problems. Do I understand correctly that problems would be: cluster $n$ training observations in a $d$-dimentsional space, then apply those clusters to new observations?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-09-14. Score: 5 | Accepted answer
  
  I disagree with the context that MNIST is the "hello world" of supervised learning. It is definitely, though, the "hello world" of **image classification**, which is a very specific sub-field of supervised learning.
  
  I'd consider the [Iris](https://archive.ics.uci.edu/ml/datasets/iris) dataset a better candidate for the "hello world" of supervised learning, with other close candidates such as the [Wine](https://archive.ics.uci.edu/ml/datasets/Wine), [Wisconsin breast cancer](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) or [Pima indians](https://www.kaggle.com/uciml/pima-indians-diabetes-database) datasets. However, as an even simpler and more alternative choice, a lot of people prefer generating [their own 2-dimensional datasets](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) so that can more intuitively understand what the different algorithms are doing. An example of this is [TensorFlow playground](https://playground.tensorflow.org).
  
  Equivalently, in unsupervised learning there are a lot of different tasks. I personally think that **clustering** is probably the task that is easier for people to understand and as such the most common intro to unsupervised learning. Here there are, as well, two options:
  
  * Using an already established dataset, e.g. [Iris](https://archive.ics.uci.edu/ml/datasets/iris) (without the labels).
  * Generating your own synthetic 2-dimensional data, to better understand how the algorithms work. An example is [this](https://github.com/djib2011/python_ml_tutorial/blob/master/notebooks/26_clustering.ipynb).
  
  
  ---
  
  