The Machine Learning Framework#

We will not dive into deep theoratical 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 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 for a more formal introduction to the framework.

Further Readings#

Work in Progress to refer to notes below.

  • Machine Learning Theory

  • 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.