Machine Learning (ML) Concept

A Machine Learning (ML) Concept is an AI concept from a machine learning domain.

• Context:
  • It can (often) be referenced by a Machine Learning Relation.
  • It can (often) be referenced by a Machine Learning Concept Referencer, such as an ML term or an ML concept record.
  • It can (often) be a member of a Machine Learning Knowledge Base (such as an ML ontology).
  • ...
• Example(s):
  • the concept of Supervised Learning.
  • the concept of Inductive Logic Programming.
  • the concept of Multi-Armed Bandit Maximization.
  • …
• Counter-Example(s):
  • a Game Theory Concept.
  • an Economics Concept.
  • a Statistics Concept.
  • a Data Analysis Concept, Data Mining Concept.
• See: Machine Learning (ML)-based System, ML Textbook, ML Encyclopedia.

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References

2017A

• (Sammut & Webb, 2017) ⇒ "Unsupervised Learning". In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA pp. 1304-1304
  • QUOTE: Unsupervised learning refers to any machine learning process that seeks to learn structure in the absence of either an identified output (cf. supervised learning) or feedback (cf. reinforcement learning). Three typical examples of unsupervised learning are clustering, association rules, and self-organizing maps.

2017B

• (Sammut & Webb, 2017) ⇒ Supervised Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA pp. 1213-1214
  • QUOTE: Supervised learning refers to any machine learning process that learns a function from an input type to an output type using data comprising examples that have both input and output values. Two typical examples of supervised learning are classification learning and regression. In these cases, the output types are respectively categorical (the classes) and numeric. Supervised learning stands in contrast to unsupervised learning, which seeks to learn structure in data, and to reinforcement learning in which sequential decision-making policies are learned from reward with no examples of “correct” behavior.

2017C

• (Stone, 2017) ⇒ Stone P. (2017) Reinforcement Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA pp. 1088-1090
  • QUOTE: Reinforcement Learning describes a large class of learning problems characteristic of autonomous agents interacting in an environment: sequential decision-making problems with delayed reward. Reinforcement-learning algorithms seek to learn a policy (mapping from states to actions) that maximizes the reward received over time.

    Unlike in supervised learning problems, in reinforcement-learning problems, there are no labeled examples of correct and incorrect behavior. However, unlike unsupervised learning problems, a reward signal can be perceived.

2016

• Martin Zinkevich. (2017). “Rules of Machine Learning: Best Practices for ML Engineering."
  • QUOTE: … Most of the problems you will face are, in fact, engineering problems. … So, the basic approach is: … make sure your pipeline is solid end to end
    • Rule #1: Don’t be afraid to launch a product without machine learning.
    • Rule #2: Make metrics design and implementation a priority.
    • Rule #3: Choose machine learning over a complex heuristic.
    • Rule #4: Keep the first model simple and get the infrastructure right.
    • Rule #5: Test the infrastructure independently from the machine learning.
    • Rule #6: Be careful about dropped data when copying pipelines.
    • Rule #7: Turn heuristics into features, or handle them externally.
    • …

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