• Self-learning systems are an important and newly emerging technique in many areas of applied science such as Applied Mathematics, Engineering, Computer Science, and Statistics.
  • In particular, self-learning systems are a disruptive approach to mathematical modeling that uses differential equations at their foundation.
  • A particular strength of this approach is that it combines numerical learning algorithms such as dynamic machine learning with differential equations to design applications that can adapt to a changing environment.
  • This approach is new and unique because it explicitly takes into account the dynamic aspects of data and allows for fast and accurate modeling of self-learning systems.
  • The primary aim of this course is to provide training in the use and development of modern numerical methods and self-learning software. Graduates will develop and apply new skills to real-world problems using mathematical ideas and techniques together with software tailored for complex networks and self-learning systems.
  • While there is a strong focus on modern applications, graduates will gain in-demand skills in mathematical modelling, problem-solving, scientific computing, dynamic machine learning, complex networks and communication of mathematical ideas to a non-technical audience.