Mathematics for Machine Learning: PCA

Imperial College London is a world-leading science and engineering university located in the heart of London. Founded in 1907, it is renowned for excellence in research, innovation and teaching across STEM fields. With over 20,000 students, including more than 8,300 international students, Imperial offers a global, diverse community and strong industry connections that enhance learning and employability.

Imperial’s academic portfolio spans research-led undergraduate and postgraduate programs across science, engineering, medicine and business, with some 102 programs available to students. The college’s reputation is reflected in exceptionally high ratings for teaching and classes, modern facilities and strong student satisfaction. Research centres and partnerships with industry provide practical experience, internships and access to cutting-edge laboratories that prepare graduates for leadership roles.

Life at Imperial combines intense academic focus with the cultural and professional opportunities of London. Campus facilities, accommodation options and student services support wellbeing and career development, while extracurricular clubs and societies foster community and leadership. For international students seeking rigorous STEM training, Imperial delivers world-class instruction, extensive research opportunities and a powerful alumni network that supports global careers.

The Mathematics for Machine Learning: PCA program at Imperial College London is a non-degree course that teaches students the mathematical foundations of Principal Component Analysis (PCA). It is an intermediate-level course that lasts several weeks and is taught in English. The main advantage of this program is that it helps students understand the mathematical concepts behind machine learning algorithms.

The curriculum covers topics such as linear algebra, multivariate calculus, and programming in python. Students will develop skills in abstract thinking, algebraic manipulation, and programming. The program includes hands-on components, such as jupyter notebooks, to help students practice and apply their knowledge.

After completing this program, students can pursue careers as Machine Learning Engineers, Data Scientists, Mathematical Modellers, or Algorithm Developers. They can work in tech companies, research institutions, or finance industries. The skills and knowledge gained in this program will help students develop and implement machine learning algorithms in their future careers.