Mathematics of Machine Learning

This course introduces the mathematical foundations of modern machine learning, emphasizing the principles that underlie data-driven algorithms.

Students will develop quantitative reasoning skills through the study of linear algebra, calculus, optimization, probability, and computational methods.

Using mathematical analysis and Python-based implementation, students will formulate, analyze, and solve problems in machine learning and data science.

Topics include vector spaces, matrices, optimization, probability distributions, regression, clustering, dimensionality reduction, neural networks, and transformer architectures.

By the end of the course, students will understand how mathematical theory informs the design, analysis, and evaluation of modern machine learning algorithms.

The course emphasizes the quantitative reasoning and computational skills used by professionals in machine learning, data science, artificial intelligence, software engineering, quantitative finance, and scientific research.