Mathematics for AI and ML

Core mathematical concepts drive AI and ML algorithms. This course covers topics like linear algebra, probability, and calculus, ensuring a strong foundation for understanding how these technologies work.

Mathematics forms the core of artificial intelligence and machine learning, providing the framework for algorithms and models. This course covers essential topics such as linear algebra for vector and matrix operations, calculus for understanding optimization, and probability for managing uncertainties in predictions. You’ll learn how these concepts are applied in AI systems, like backpropagation in neural networks and clustering in data analysis. Real-world examples will illustrate how mathematical theories support decision-making models and improve accuracy. A strong grasp of these mathematical foundations is critical for designing robust AI and ML systems, bridging the gap between theory and implementation.