Maths for Machine Learning

Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on ‘automatic’, that is, machine learning is about general-purpose methodologies that can be applied to many sets of data while producing something meaningful. In this article, I will take you through why you need to learn some maths for machine learning and some important topics of maths that you need to learn.

Why Maths for Machine Learning?

Because machine learning is inherently data-driven, data acts as the heart of machine learning algorithms. The goal of machine learning is to design general-purpose methodologies to extract valuable models from data, ideally without a lot of domain-specific expertise. To achieve this goal, we design models that are generally related to the process that generates data, similar to the model of the dataset provided to us.

Also, Read – Model Validation in Machine Learning.

A model learns from data if its performance on a given task improves after taking the data into account. The goal is to find good models that generalize well to still invisible data, which we might care about in the future. Learning can be understood as a means of learning to automatically find patterns and structure in data by optimizing model parameters.

To stay in the field of machine learning for a longer time, I believe that the maths for machine learning is very important for understanding the fundamentals on which more complex machine learning systems are built.

There are many more reasons why maths for machine learning is important, some of the reasons are:

  • Selecting the right algorithm which includes considerations on the accuracy, learning time, model complexity, number of parameters and number of features
  • Choice of parameter settings and validation strategies.
  • Estimation of the correct confidence interval and uncertainty.

The Maths You Need For Machine Learning

The main question when trying to understand an interdisciplinary field such as machine learning is how much math is needed and what level of math is needed to understand these techniques. 

My answer to this question is that I think you need some minimum level of maths to be an expert in machine learning and you should be familiar with the importance of each mathematical concept. The important topics of maths are mentioned below that you need for Machine Learning.


Linear Algebra:

Linear algebra is the study of vectors and linear functions. In broad terms, vectors are things you can add and linear functions are functions of vectors that respect vector addition. The goal of Linear Algebra is to teach you to organize information about vector spaces in a way that makes problems involving linear functions of many variables easy.

Probability Theory and Statistics:

Probability is the study of random events. It is used to analyze genetics, weather forecasts, and a myriad of other everyday events. Statistics is the mathematics we use to collect, organize and interpret numerical data. It is used to describe and analyze sets of test results, election results, and buyer preferences for particular products.

Multivariate Calculus:

Multivariate calculus is used in regression analysis to derive formulas for estimating relationships between various sets of empirical data. Multivariate calculus is used in many fields of the natural and social sciences and engineering to model and study high-dimensional systems that exhibit deterministic behaviour.

Algorithms and Complex Optimizations:

This is important to understand the computational efficiency and scalability of our machine learning algorithm and to exploit the scarcity of our datasets. Knowledge of data structures, dynamic programming, random and sublinear algorithm, graphics, gradient, and Primal-Dual methods are required.


This includes other maths topics for machine learning that are not covered in the four main areas described above. They include real and complex analysis, information theory (entropy, information gain), functional spaces and collectors.

So, hope you liked this article on maths for machine learning and all the necessary topics you need. For beginners, you don’t need a lot of maths to start machine learning. As you begin to develop in this industry, you will need knowledge of all of the above topics to build more complex machine learning systems. You can download this book to learn maths for machine learning. Please feel free to ask your valuable questions in the comments section below.

Also, Read – Machine Learning Projects for Healthcare.

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Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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