Machine Learning theory is a field that meets statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data which can be used to build intelligent applications.

There are various reasons why the mathematics of Machine Learning is necessary, and some of them are highlighted below:

Selecting the appropriate algorithm for the problem includes considerations of accuracy, training time, model complexity, the number of parameters and number of characteristics.

• Identifying underfitting and overfitting by following the Bias-Variance tradeoff.
• Choosing parameter settings and validation strategies.
• Estimating the right determination period and uncertainty.

Level of Maths Needed

The foremost question when trying to understand a field such as Machine Learning is the amount of maths necessary and the complexity of maths required to understand these systems.

The answer to this question is multidimensional and depends on the level and interest of the individual.

Here is the minimum level of mathematics that is needed for Machine Learning Engineers / Data Scientists.

1. Linear Algebra (Matrix Operations, Projections, Factorisation, Symmetric Matrices, Orthogonalisation)
2. Probability Theory and Statistics (Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions.)
3. Calculus (Differential and Integral Calculus, Partial Derivatives)
4. Algorithms and Complex Optimizations (Binary Trees, Hashing, Heap, Stack)