Imperial’s Department of Mathematics has introduced a new machine learning stream in its Applied Mathematics MSc program. Starting in 2024, the Scientific Computing and Machine Learning (SCML) stream aims to provide students with a solid foundation in computational mathematics and data-driven modeling. The course, directed by Dr. Dante Kalise, is designed to be different from traditional computing or data science programs. Professor Colin Cotter noted the importance of machine learning in various application areas, from quantum mechanics to ocean flow modeling. The SCML stream responds to the growing demand for professionals skilled in both theoretical and practical aspects of scientific computation and machine learning.
Introduction to the New Machine Learning Stream in Applied Mathematics MSc at Imperial
Imperial’s Department of Mathematics has recently announced an addition to its Applied Mathematics MSc program. This new stream, Scientific Computing and Machine Learning (SCML), will commence in the academic year 2024. The SCML stream is designed to equip students with a solid understanding of the convergence of contemporary computational mathematics and data-driven modeling.
The SCML stream is unique in its approach, offering a different perspective than traditional computing or data science programs focusing on machine learning. Dr Dante Kalise, the course director of the Applied Mathematics MSc, emphasizes that the SCML stream will provide a distinct learning experience for students.
Core and Optional Modules in the SCML Stream
Students who choose the SCML stream must take core modules that lay the groundwork for scientific computing and machine learning. These core modules include subjects like computational linear algebra. In addition to these core modules, students will be free to select from a range of optional modules offered by the Department, allowing for a customized educational journey.
The flexibility of the SCML stream allows students to integrate their core modules with other areas of applied mathematics. This could include domains such as dynamical systems mathematics or mathematical physics, providing a comprehensive and diverse learning experience.
The Role of Machine Learning in Applied Mathematics
The introduction of the SCML stream reflects the growing significance of machine learning across various application areas within applied mathematics. Professor Colin Cotter notes that machine learning has become a major theme in a wide range of applications, from quantum mechanics to modeling ocean flows.
However, Professor Cotter also highlights the importance of understanding the limitations of machine learning solutions. For instance, while a neural network may provide a solution to a problem, it is crucial to apply mathematical principles to comprehend the constraints of these solutions.
Preparing Students for the Future Job Market
The SCML stream responds to the rising demand for professionals proficient in both the theoretical and practical aspects of scientific computation and machine learning. Dr Kalise believes that the SCML stream will prepare students for a highly competitive job market, regardless of whether they aspire to work in big tech or scientific research.
The Applied Mathematics MSc website provides further details for prospective students and those interested in learning more about the SCML stream, its curriculum, and application procedures.
