Handbook

The course covers theoretical foundations necessary for the in-depth understanding of modern optimisation methods for data science. The optimisation methods are presented in the context of relevant applications, such as the training of artificial neural networks and data classification. The methods discussed in the course include (stochastic) gradient descent, projection and splitting techniques. The course prepares students for confident application of modern numerical methods to problems in data science and helps them build sufficient mastery of optimisation tools and techniques for designing and implementing tailored methods for solving new problems.