I’m an AI researcher, and I’ve received quite a few emails asking me just how much math is required in Artificial Intelligence.

I won’t lie: **it’s a lot of math**.

And this is one of the reasons AI puts off many beginners. After much research and talks with several veterans in the field, I’ve compiled this no-nonsense guide that covers all of the fundamentals of the math you’ll need to know.

The concepts mentioned below are usually covered over several semesters in college, but I’ve boiled them down to the core principles that you can focus on.

This guide is an absolute life-saver for beginners, so you can study the topics that matter most. But it’s an even better resource for practitioners, such as myself, who require a quick breeze-through on these concepts.

Note: You don’t need to know all of the concepts (below) in order to get your first job in AI. All you need is a

firm graspof the fundamentals. Focus on those and consolidate them.

You can also find these resources on my Github: Jason’s AI Math Roadmap.

## 1. Algebra You Need to Know for AI

Knowledge of algebra is perhaps fundamental to math in general. Besides mathematical operations like addition, subtraction, multiplication and division, you’ll need to know the following:

## 2. Linear Algebra You Need to Know for AI

Linear Algebra is the primary mathematical computation tool in Artificial Intelligence and in many other areas of Science and Engineering. With this field, you need to understand 4 primary mathematical objects and their properties:

- Scalars
- Vectors — a list of numbers, arranged in order. Consider them as points in space with each element representing the coordinate along an axis.
- Matrices — a 2-D array of numbers where each number is identified by 2 indices.
- Tensors
- Eigenvectors & Eigenvalues — special vectors and their corresponding scalar quantity. Understand the significance and how to find them.
- Singular Value Decomposition
- Principal Component Analysis (PCA) — understand the significance, properties, and applications.

Properties such as the Dot product, Vector product and the Hadamard product are useful to know as well.

## 3. Calculus You Need to Know for AI

Calculus deals with changes in parameters, functions, errors and approximations. Working knowledge of multi-dimensional calculus is imperative in Artificial Intelligence.

The following are the most important concepts (albeit non-exhaustive) in Calculus:

- Derivatives — rules (addition, product, chain rule, and so on), hyperbolic derivatives (tanh, cosh, and so on) and partial derivatives.
- Vector/Matrix Calculus — different derivative operators (Gradient, Jacobian, Hessian and Laplacian)
- Gradient Algorithms

## 4. Statistics & Probability Concepts You Need to Know for AI

This topic will probably take up a significant chunk of your time. Good news: these concepts aren’t difficult, so there’s no reason why you shouldn’t master them.

- Basic Statistics — Mean, median, mode, variance, covariance, and so on.
- Basic rules in probability
- Random variables
**Bayes’ Theorem**— calculates validity of beliefs. Bayesian software helps machines recognize patterns and make decisions.- Maximum Likelihood Estimation (MLE)
- Common Distributions — binomial, poisson, bernoulli, gaussian, exponential.

## 5. Information Theory Concepts You Need to Know for AI

This is an important field that has made significant contributions to AI and Deep Learning, and is yet unknown to many. Think of it as an amalgamation of calculus, statistics, and probability.

- Entropy
- Cross-Entropy
- Kullback Leibler Divergence
- Viterbi Algorithm
- Encoder-Decoder
**—**used in Machine Translation RNNs and other models.

## Math is Fun!

If you are terrified at the mere mention of “math”, you’re probably not going to have much fun in Artificial Intelligence.

But if you’re willing to invest time to improve your familiarity with the principles underlying calculus, linear algebra, stats, and probability, nothing — not even math — should get in the way of you getting into AI.

PS: Math *really* is fun. As you go deeper into math, be sure to understand the beauty of a certain math concept and *how* it affects something. You’ll soon share the unbridled passion that many mathematicians and AI Scientists have!

A tip: Treat mathematical concepts as a pay-as-you-go: whenever a foreign concept pops up, grab it and devour it! The guide above presents a minimal, yet comprehensive, resource to understand any kind of topic or concept in AI.

Be sure to follow me on Twitter for updates on future articles. Happy learning!