Difference between revisions of "Machine Learning"
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Machine Learning is | {{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] | ||
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: | |||
# [[Supervised learning]] | |||
## Regression | |||
## Classification | |||
# [[Unsupervised learning]] | |||
## Clustering | |||
## Anomaly Detection | |||
## Compression | |||
# [[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>. | |||
<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:
- Supervised learning
- Regression
- Classification
- Unsupervised learning
- Clustering
- Anomaly Detection
- Compression
- 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[1].
References
- ↑ 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.