12 Essential Math Theories for AI Understanding AI requires a foundation in core mathematical concepts. Here are twelve key theories that deepen your AI knowledge: Curse of Dimensionality: Challenges with high-dimensional data. Law of Large Numbers: Reliability improves with larger datasets. Central Limit Theorem: Sample means approach a normal distribution. Bayes' Theorem: Updates probabilities with new data. Overfitting & Underfitting: Finding balance in model complexity. Gradient Descent: Optimizes model performance. Information Theory: Efficient data compression. Markov Decision Processes: Models for decision-making. Game Theory: Insights on agent interactions. Statistical Learning Theory: Basis for prediction models. Hebbian Theory: Neural networks learning principles. Convolution: Image processing in AI. Familiarity with these theories will greatly enhance understanding of AI development and its underlying principles. Each concept builds a foundation for advanced topics and applications.