COURSE CURRICULUM

Year 1 of this program provides a strong foundation in key mathematical principles and tools. The Calculus I module introduces essential concepts like limits, derivatives, integrals, and infinite series, emphasizing problem-solving skills. In Computational Mathematics, students gain proficiency in Python and MATLAB, learning to apply these tools to solve mathematical problems and visualize data. The Linear Algebra module offers a deep dive into matrices, vectors, and eigen theory, fostering both geometric and algebraic understanding. The Mathematical Foundations module strengthens logical reasoning and proof techniques, covering concepts such as sets, functions, ciphers, and complex numbers. Mathematical Models introduces students to applying mathematics to real-world scenarios, including biology, ecology, and physics, while also teaching numerical techniques for solving more complex problems. Finally, Statistical Theory and Methods I focuses on statistical inference, probability, and regression analysis, using tools like Minitab for data analysis.In Year 2, students deepen their mathematical knowledge with applied skills. The Applications of Mathematics and Graduate Skills module provides an opportunity to apply mathematical techniques to real-world problems and improve career readiness. Calculus II builds on the foundation of single-variable calculus, introducing multivariable calculus, Fourier series, and advanced differential equations. In Mathematical Methods for Machine Learning, students explore the intersection of mathematics and machine learning, studying algorithms for building supervised and unsupervised models.Year 3 advances students' expertise in machine learning and statistical techniques. The Advanced Machine Learning module focuses on working with large, high-dimensional data sets using industry-standard tools like PyTorch, while deepening understanding of neural networks and deep learning. In Statistical Learning, students apply statistical methods such as regression, clustering, and principal components to real-world datasets using programming languages like R or Python. This progression equips students with the skills needed to excel in modern data-driven industries.