Machine Learning

Let’s look at some examples of Machine Learning.

Try to answer some questions

What language is this?

LanguageIdentify takes pieces of text, and identifies what human language they’re in.
Identify the language each phrase is in:
In[]:=
LanguageIdentify"thank you","merci","dar las gracias","感謝","благодарить"

What’s in this image?

Identify what an image is of:
In[]:=
ImageIdentify
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What sort of sentiment does this text express?

Classifying the “sentiment” of text:
In[]:=
Classify["Sentiment","I'm so excited to be programming"]
In[]:=
Classify["Sentiment","math can be really hard"]

Reframe the questions: Is this A or B (or C or D or E)
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Remember Vectors and Vector Spaces
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Nearness as step to identifying
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Supervised vs. Unsupervised Machine Learning

In machine learning, one often gives training that explicitly says, for example, “this is a cheetah”, “this is a lion”. This is known as “Supervised Learning”. You provide labeled examples that were created by some expert.
But one also often just wants to automatically pick out categories of things without any providing specific labels. This is “Unsupervised Learning”.

Supervised Learning

This is used to answer questions of the type:
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  • Is this A or B (or A or B or C or D or E)? (Classification)
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  • How much or how many? (Regression)

    • The Task of Classification
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    The Task of Regression
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    Unsupervised Learning

    This is used to answer questions like:
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  • How is the data organized?
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  • Do the samples separate into groups of some kind?
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  • Are there samples that are very different from most of the group (outliers)?

    • The Task of Clustering
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    Classification based on Clustering
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    AI, Machine Learning and Neural Networks
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