Applied Mathematics for Machine Learning

Mathematics is an essential foundation for machine learning. This course introduces matrix algebra, differential calculus, and optimization techniques, providing students with the mathematical tools needed to understand machine learning and artificial intelligence models. Students solve systems of equations, perform matrix operations, find eigenvalues and eigenvectors and quadratic forms, and compute singular value decomposition. They analyze graphs of functions, compute derivatives, partial derivatives, and gradient vectors. Students learn approximations techniques, Newton’s method, and gradient descent commonly used in basic machine-learning problems.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics, Post-Degree Diploma in Data Analytics (Work Experience), and Post-Degree Certificate in Data Analytics.