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This course provides a rigorous yet accessible introduction to the essential mathematical foundations underlying modern Artificial Intelligence (AI) and Deep Learning applications. The course emphasizes the practical application of linear algebra, probability, statistics, calculus, and optimization techniques in the design and understanding of machine learning systems. Students will explore how these core mathematical tools are used to build models for computer vision, regression, classification, clustering, and deep neural networks. Each topic is contextualized with real-world problems, Python Code, and bridging theory with implementation. The course is designed for students from diverse academic backgrounds who want to gain a solid foundation in mathematics for working with AI systems. Topics include: Vectors, matrices, and tensor operations; Calculus and gradient-based optimization for training neural networks; Probability theory and statistical inference in machine learning; Mathematical intuition behind computer vision, regression, classification, clustering, and deep neural networks with practical use cases.