Mathematics For Machine Learning

This course provides a comprehensive foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms from first principles. Students will explore Linear Algebra, covering vectors, matrices, eigenvalues, and singular value decomposition-critical for data representation and transformations. Multivariable Calculus will focus on gradients, Jacobians, and Hessians, which are fundamental to optimization techniques used in training models.

The course also introduces Probability and Statistics, covering key topics such as random variables, probability distributions, expectation, variance, and fundamental statistical inference techniques. Optimization methods, including gradient descent and related algorithms, will be explored to understand how machine learning models learn from data. Additionally, students will develop problem-solving skills by working through mathematical proofs and derivations that underpin these techniques.

Throughout the course, students will gain hands-on experience with NumPy and SciPy, leveraging these powerful Python libraries to implement mathematical concepts programmatically. Rather than applying models to real-world datasets, the focus will be on understanding and building the mathematical foundations necessary for machine learning. By the end of the course, students will have the necessary mathematical and computational tools to derive and implement machine learning techniques from scratch, preparing them for deeper study in artificial intelligence and data science, as well as advanced mathematical modeling.