<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>

CC BY-SA 4.0
2020-09-14T09:48:56.537Z
2020-09-14T09:48:56.537Z
Djib2011
<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>

CC BY-SA 4.0
2020-09-14T06:32:41.943Z
Probably knmeans clustering or gaussian clustering
FourierFlux
CC BY-SA 4.0
2020-09-14T06:45:42.550Z
@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?
Christian Aichinger
<unsupervised-learning>
What is the “Hello World” problem of Unsupervised Learning?
Christian Aichinger
# 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**: @username_1 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?  

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## 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).


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