Abstract
Machine Learning theory is a multidisciplinary field with intersection of statistics, probability, computer science and mathematics. A thorough understanding of probability and statistics theorem is necessary for a good grasp of the underlying work of algorithms. Probability is a language in mathematics used to measure the likelihood of events. In order to quantify and solve machine learning problems concisely, the scientists decide to use probability theory as the language for describing the uncertainties in machine learning problems. In this chapter, we will learn different interpretations for event probability, basics concepts in probability theory and example about applications of probability theory in machine learning.
It's a multi-part series in which I am planning to cover the following:
- What is Probability?
- Probability Notation
- Basic Sets Operation
- General Rules in Probability