In Uniform Convergence Learning, The Expectation of the Loss from any Learner Approaches Zero for Large Enough Training Set Size This post explains why for hythothesis class with uniform convergence property, the expectation of the loss from any learner approaches zero for large enough sample size.

Proof of $E(X) = \int_0^\infty (1-F(x)) \ dx$ where $F(x)$ denotes CDF of the random variable $X$ with range $[0, \infty)$ This post shows how to prove the equality $E(X) = \int_0^\infty (1-F(x)) \ dx$.

Unpacking the Definition of the Sample Complexity Function and Some of Its Properties This post will take a closer look at the definition of sample complexity function for a give hypothesis class and some of its properties

On the Definition of Uniform Convergence Property and Representativeness This post reviews the definition of uniform convergence property of a hypothesis class and the idea of the representativeness of a training sample covered in the book "Understanding Machine Learning" written by Ben David S. et al.

Another Proof of a Useful Proposition in Proving Sauer's Lemma This post shows another proof of a claim that the cardinality of a class of sets cannot exceed the cardinality of the set containning the class's shattered sets.

More on the Definition of a Set That Is Shattered by a Class of Sets This post reviews the definition a set that is shattered by a class of sets and discusses several different ways to look at the definition.

Shatter Sets, Growth Functions & VC Dimension This post summarizes three basic concepts in Machine Learning: the shatter set, the growth function and the VC dimension.