Difference between revisions of "Machine Learning"

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## Model Based
## Model Based
## Transfer Learning
## 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>

Revision as of 10:08, 23 February 2022

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

Machine Learning is a field derived from Computing Science.

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.

{{#ev:youtube|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