The Role of Mathematics in Artificial Intelligence

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Many people think that AI is only about coding, but mathematics is actually a fundamental part of AI.

AI = Math + Coding, and they are both essential. AI is impossible without math or coding.

However, training AI does not necessarily require a lot of mathematical knowledge. Most people train AI without fully understanding the mathematical algorithms behind it. But it is better if you know how AI works with mathematical algorithms.

In fact, AI is based on a mathematical model. Some mathematical functions and algorithms are used in the model. It sounds abstract, but you can basically imagine it as using calculus to optimize something.

So, how do we train an AI?

First, we use advanced mathematics to model an AI, then we use coding to train the AI because the process of training AI is tedious, so we let our computer handle all the training stuff. That is why learning mathematics can help you understand AI more deeply, not just coding.

Linear algebra, vector calculus, and advanced statistics are mainly used in AI algorithms. These three are also known as the three big monsters in AI. Moreover, if you want to fully understand how AI algorithms work, you need to study abstract algebra. Mathematics in AI is not just calculation, but also analysis and proof.

Here, I give you a simple example (training a perceptron to do OR calculations) to help you understand how AI works in mathematical language.

I trained the AI using only the math package in Python, without any extra packages (NumPy, SciPy, scikit-learn, TensorFlow, PyTorch, Pandas, etc). This way, you can understand my code more easily.

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