Why do we need math for AI?
The relationship between math and Artificial Intelligence is crucial to the area, it is not always obvious to everybody. If you are interested in working with AI or subsets such as Machine Learning and Deep Learning, understanding math is not just useful, but is essential.
Although tools and platforms have made it easier to create basic AI applications, building robust, efficient, and ethical solutions requires technical expertise — especially in mathematics, statistics, and programming.
Why is math important for AI?
AI problems are classified into two main categories: search and knowledge representation. However, with the evolution of Machine Learning, many current applications involve statistical modeling, optimization, and inference from data.
Mathematics develops logical reasoning, critical thinking, and precision. It instills discipline and offers principles that remain relevant as technology evolves. In AI, a strong math foundation helps define problems and provides effective solutions.
What math is crucial in AI?
- Linear Algebra
Enables machines to process large datasets using matrices, vectors, and tensors, which represent images, text, and other data.
- Calculus
Calculus, especially differential calculus, is used to optimize models through techniques like gradient descent. Concepts such as partial derivatives and Jacobians are crucial for training neural networks.
- Probability and Statistics
AI constantly faces uncertainty. Probability enables machines to make predictions and informed decisions. Statistics help analyze and learn from data.
A solid foundation in mathematics, software engineering, and computer science is essential for building and optimizing AI models.
I will be launching a series on mathematics in AI to support those interested in advancing their knowledge in this field.