Foundational Mathematics for AI

This course offers a comprehensive introduction to the mathematical principles that form the foundation of artificial intelligence and machine learning. Designed for learners with a variety of academic backgrounds, the course bridges essential mathematical concepts with real-world AI applications, empowering students to understand and implement mathematical techniques critical for AI development. By the end of this course, learners will be able to apply functions, matrices, and vectors to represent and analyze data relationships. Students will be able to use descriptive statistics and visualization techniques to explore and summarize datasets, solve systems of linear equations and model complex relationships using linear regression of single and multiple variables, and understand and implement foundational principles of probability, including Bayes' Theorem. The course builds to advanced mathematical techniques in Calculus, and develops derivatives and integrals to analyze rates of change and distributions, essential for optimization and modeling in AI. Concepts from Linear Algebra are used to explore advanced concepts like eigenvectors, determinants, and linear transformations for dimensionality reduction and classification algorithms. This course is specifically tailored for aspiring AI practitioners. Unlike traditional math courses, this curriculum focuses on mathematical techniques directly applicable to artificial intelligence and machine learning, bridging theory with practice. Through interactive modules, real-world datasets, and tools like Python and Excel, you’ll not only understand the concepts but also apply them to solve practical problems. With clearly defined modules such as Descriptive Statistics, Linear Algebra, Probability, and Optimization, this course allows you to build knowledge progressively while connecting each concept to AI use cases. Each topic is introduced with AI-related examples, like using linear regression to model salaries or a