5 Crucial Mathematics Skills To Study Artificial Intelligence

The recent boom of Artificial Intelligence technologies has changed the academic world for the long haul. More and more students want to enter the field. With businesses looking for AI tools such as facial recognition, conversational chatbots, content generators, and research apps, the demand for AI professionals is surging. Not to mention, it is a new and interesting subject for STEM enthusiasts, making it a hot favorite among them. 

Statistics show that the global AI market is likely to snowball in the coming years, reaching a value of a whopping $190.61 billion in 2025. At the same time, a lack of trained and experienced professionals is an expected constraint for the market’s growth. It translates into a golden opportunity for aspiring professionals. 

The picture also looks good on the money front. The average wage for AI workers in the US is $102,980, and it is growing at an impressive rate of 2.82% per annum. Students opting for the skill can make the most of the opportunities by filling the gap and mastering the relevant skills.

Before diving in, you must understand that studying AI takes a lot of commitment because it takes your STEM skills a notch higher. The subject requires an in-depth understanding of mathematics concepts, many of which form the foundation of machine learning and artificial intelligence. While your bachelor’s or master’s degree will include these concepts, you can get a head start by studying them early.

Here are the mathematics skills that can help you study and ace AI courses. 


According to Machine Learning Mastery, multi-dimensional calculus plays a significant role in comprehending the internal working of machine learning algorithms. It deals with the changes in functions, parameters, errors, and approximations. The technique includes differentiation and integration of functions with more than one variable. It is deployed during feature engineering, the most sensitive part of AI projects. Here are the most vital calculus concepts for AI learners:

  • Derivatives: Rules, partial derivatives, and hyperbolic derivatives
  • Vector/Matrix calculus: Derivative operators such as Gradient, Jacobian, Laplacian, and Hessian
  • Gradient Algorithms: Local/global maxima and minima, stochastic gradient descent, saddle points, convex functions, and performance comparison

With Proprep maths courses, students can explore the concepts early with a pre-calculus course and move to the advanced levels of study with Calculus I, II, and III. These courses are available in the form of bite-sized personalized videos, making concepts simple to understand and easy to remember.

Statistics and Probability

Statistics and probability hold the AI projects together, so learning these concepts is essential for students planning to study artificial intelligence and pursue a career in it down the road. These functions help in quantifying things and identifying patterns in data. Both are critical as the AI systems work with tons of data. Here are the concepts you need to master:

  • Basic statistics: Mean, mode, median, variance, and covariance
  • Basic rules in probability: Events, sample spaces, conditional probability
  • Common distributions: Binomial, poisson, gaussian, exponential, and bernoulli
  • Random variables: Continuous and discrete, variance, expectation, and distributions (joint and conditional)
  • Maximum Likelihood Estimation (MLE)
  • Bayes’ Theorem

Linear Algebra

Another math skill you need to understand and master to pursue AI is linear algebra. It helps with steps such as data preprocessing, data transformation, and model evaluation. Here are the key topics that AI students must be familiar with:

  • Scalars
  • Vectors
  • Matrices
  • The determinant of a matrix
  • The inverse of a matrix
  • Transpose of a matrix
  • Eigenvalues
  • Eigenvectors
  • Dot product
  • Tensors
  • Single value Decomposition
  • Principal Component Analysis

Optimization Methods

Most machine learning algorithms use predictive modeling minimizing an objective function and finding the weights to be applied to the testing data. Optimization methods help with these calculations, making them an essential mathematics skill for AI professionals. The main topics you must know include:

  • Cost function/Objective function
  • Error function
  • Likelihood function
  • Gradient Descent Algorithm and its variants

Information Theory

A relatively lesser-known math skill for AI students is information theory, which is an amalgamation of statistics, probability, and calculus. The concepts of information theory contribute to AI and Deep Learning, making their knowledge an extra advantage. Here are the key topics to study:

  • Entropy
  • Cross-Entropy
  • Encoder-Decoder
  • Kullback Leibler Divergence 
  • Viterbi Algorithm


On the surface,  mathematics may not seem to have much to do with machine learning and artificial intelligence. But you can discover a strong connection by scratching the surface. Many mathematical concepts form the foundation of AI algorithms. 

Knowing and understanding them can give you a head start with your coursework. Focus on learning these concepts early to shape your higher education and build a successful career in artificial intelligence.