Difference between revisions of "Machine Learning"

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Machine Learning is a field derived from [[Parent Domain::Computing Science]].
{{WikiEntry|key=Machine Learning|qCode=2539}} is the scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions.


[https://www.turing.org.uk/scrapbook/test.html The Alan Turing Internet Scrapbook]
[https://www.turing.org.uk/scrapbook/test.html The Alan Turing Internet Scrapbook]
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All computation or decision-making processes can be considered as a scientific approximation of real-world events in spacetime. The notion of [[Abstract Interpretation|Abstract Interpretation (AI)]], also known as a field of to efficiently approximate decision/computational procedures. Clearly, this is different from [[wikipedia:Artificial Intelligence|Artificial Intelligence]], also abbreviate as [[wikipedia:Artificial Intelligence|AI]].
All computation or decision-making processes can be considered as a scientific approximation of real-world events in spacetime. The notion of [[Abstract Interpretation|Abstract Interpretation (AI)]], also known as a field of to efficiently approximate decision/computational procedures. Clearly, this is different from [[wikipedia:Artificial Intelligence|Artificial Intelligence]], also abbreviate as [[wikipedia:Artificial Intelligence|AI]].


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As Prof. Lazarus has presented, there is 3 main kinds of algorithms typically used to accomplished machine learning, which you might use to process your data; depending on which phase your are in or what you hope to accomplish you might end up using all these options:
As Prof. Lazarus has presented, there is 3 main kinds of algorithms typically used to accomplished machine learning, which you might use to process your data; depending on which phase your are in or what you hope to accomplish you might end up using all these options:


# [[Supervised learning]]
# [[Supervised learning]]
## Regression
## Classification
# [[Unsupervised learning]]
# [[Unsupervised learning]]
## Clustering
## Anomaly Detection
## Compression
# [[Reinforcement learning]]
# [[Reinforcement learning]]
## Model Free
## Model Based
## Transfer Learning
=Important Publications=
One of the key idea in machine learning came from the [[PAC]] assumption by [[Leslie Valiant]]. His ideas can be found in this book<ref>{{:Book/Probably Approximately Correct}}</ref>.


[[Category:Technical Term]]
<noinclude>
=References=
<references/>
=Related Pages=
[[Category:AI]]
</noinclude>

Latest revision as of 08:44, 13 January 2024

Machine Learning
Term Machine Learning
Knowledge Domain Computing Science, Artificial Intelligence
Parent Domain Cognitive Science


Machine Learning(Q2539) is the scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions.

The Alan Turing Internet Scrapbook

All computation or decision-making processes can be considered as a scientific approximation of real-world events in spacetime. The notion of Abstract Interpretation (AI), also known as a field of to efficiently approximate decision/computational procedures. Clearly, this is different from Artificial Intelligence, also abbreviate as AI.

|https://www.youtube.com/watch?v=wGswp5A0jkQ%7C%7C%7C%7C%7C}}

As Prof. Lazarus has presented, there is 3 main kinds of algorithms typically used to accomplished machine learning, which you might use to process your data; depending on which phase your are in or what you hope to accomplish you might end up using all these options:

  1. Supervised learning
    1. Regression
    2. Classification
  2. Unsupervised learning
    1. Clustering
    2. Anomaly Detection
    3. Compression
  3. Reinforcement learning
    1. Model Free
    2. Model Based
    3. Transfer Learning

Important Publications

One of the key idea in machine learning came from the PAC assumption by Leslie Valiant. His ideas can be found in this book[1].


References

  1. Valiant, Leslie (2013). Probably Approximately Correct - Nature’s Algorithms for Learning and Prospering in a Complex World. local page: Basic Books. ISBN 978-0-465-03271-6. 

Related Pages