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May 26, 2020
We address the importance of dimensionality in machine learning.
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May 5, 2020
We derive Hoeffding's inequality. This is one of the most used results in machine learning theory.
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May 2, 2020
The Rademacher complexity measures how a hypothesis correlates with noise. This gives a way to evaluate the capacity or complexity of a hypothesis class.
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May 1, 2020
We study binary classification problem in R**d using hyperplanes. We show that the VC dimension is d+1.
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Apr 30, 2020
The VC dimension is a fundamental concept in machine learning theory. It gives a measure of complexity based on combinatorial aspects. This concept is used to show how certain infinite hypothesis classes are PAC-learnable. Some of the main ideas are explained: growth function and shattering. I give examples and show how the VC dimension can bound the generalization error.
