AI is a fascinating and deeply complex topic requiring in-depth understanding of advanced maths topics. While not all positions around AI, whether it is an AI engineer, NLP developer, ML data analyst, etc, require the most in-depth understanding of the underlying mathematical concepts, some positions will require significant understanding. Here are some of them of the relevant maths concepts:
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Linear algebra - embeddings, which are vector representations of words, are hugely important in AI, including for large language models and natural language processing
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Probability and statistics - greatly important to AI and machine learning, you will need a strong grasp of random variables, distributions such as normal and binomial, Bayes Theorem, etc
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Calculus - very important for AI and forms the basis for a large part of the theory behind AI, you will need a strong grasp in various fields of calculus, including derivatives, gradient descent and activation functions
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Graph theory - have a wide range of applications, also form the basis of graph machine learning and graph neural networks
