# The Machine Learning Framework

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We will not dive into deep theoretical frameworks such as PAC learning, VC
dimension, etc. Instead, we will do a gentle introduction using basic
probability.

## The Naive Probabilistic Framework

This section details the mathematical framework that we will use throughout. It
is naive because it is not written rigorously. But for our purpose, this is
sufficient to gain a good understanding of the concepts.

[This article here](https://mostafa-samir.github.io/ml-theory-pt1/) really lays
out the framework in an intuitive manner. Please read this before proceeding.
Alongside with Alexander Jung's book, Machine Learning: The Basics, this should
give anyone a solid foundation in how to think about machine learning.

## More Formal Framework

Read [Foundations of Machine Learning](https://cs.nyu.edu/~mohri/mlbook/) for a
more formal introduction to the framework.

## Further Readings

**Work in Progress** to refer to notes below.

-   [Machine Learning Theory](https://mostafa-samir.github.io)
-   Jung, Alexander. Machine Learning: The Basics. Springer Nature
    Singapore, 2023.
-   Mohri, Mehryar, Rostamizadeh Afshi and Talwalkar Ameet. Foundations of
    Machine Learning. The MIT Press, 2018.