A Gentle Introduction to Computational Learning Theory
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Last Updated on September 7, 2020
Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms.
These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having a high-level understanding of some of the more prominent methods may provide insight into the broader task of learning from data.
In this post, you will discover a gentle introduction to computational learning theory for machine learning.
After reading this post, you will know:
- Computational learning theory uses formal methods to study learning tasks and learning algorithms.
- PAC learning provides a way to quantify the computational difficulty of a machine learning task.
- VC Dimension provides a way to quantify the computational capacity of a machine learning algorithm.
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