Imperial introduces new machine learning stream in Applied Mathematics MSc


Two students, smiling and enjoying a practical session in a computer lab in the Department of Mathematics.

In an enhancement to its Applied Mathematics MSc, the Department of Mathematics has launched a stream for scientific computing and machine learning.

Starting from the academic year 2024, the SCML stream aims to provide students with a robust foundation of the intersection of modern computational mathematics and data-driven modelling.

Students are introduced to SCML concepts, specifically in contexts that they may encounter in fields of applied mathematics, such as biomathematics or fluid mechanics.

What we’ll offer is different from what students may get from traditional computing or data science programmes on machine learning. Dr Dante Kalise Department of Mathematics

“What we’ll offer is different from what students may get from traditional computing or data science programmes on machine learning,” said Dr Dante Kalise, course director of the Applied Mathematics MSc.

Candidates opting for the SCML stream must select core modules that teach the foundations of scientific computing and machine learning, such as computational linear algebra.

The other half of a SCML student’s modules can be selected from the other optional modules that the Department offers, allowing for a tailored educational experience.

Students are able to combine their core SCML modules with other applied mathematics domains, such as dynamical systems mathematics or mathematical physics.

“In applied mathematics, we’re interested in lots of different applications, from quantum mechanics to modelling ocean flows,” said Professor Colin Cotter, “What we see is that machine learning is now a big theme in all of these application areas.”

“If you have a neural network, for example, it can be a bit of a closed box. It may give you a solution to a problem but there needs to be an effort to use applied mathematics to understand the limitations of those solutions,” Professor Cotter said.

The new SCML stream responds to the increasing demand for professionals skilled in both the theoretical and practical aspects of scientific computation and machine learning.

Dr Kalise said, “The stream will prepare students for an increasingly competitive job market, no matter whether they want to work in big tech or scientific research.”

Prospective students and those interested in further details about the SCML stream, its curriculum, and application procedures are encouraged to visit the Applied Mathematics MSc website.


Jacklin Kwan

Jacklin Kwan
Faculty of Natural Sciences


Education, Big-data, Artificial-intelligence
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