Statistical Deep Learning

About this programme

Topics treated include feedforward networks, regularization and optimization of networks with many layers, convolutional networks, recurrent networks and validation methods. In addition, mathematical interpretations of networks are given, such as nonlinear regression with different link functions for the outcome variable. The course includes some of the following topics; autoencoders, representation learning, deep generative methods, and information theoretic concepts of deep learning. Instruction is given in the form of lectures, exercise sessions and supervision. To follow your passion. To be close to international career opportunities. To explore city and countryside. In Stockholm you can really get the best of both worlds. Here we explain all the different steps you need to go through when applying to a course or programme. Stockholm University arranges regular webinars and participates in educational fairs and events virtually and around the world to meet students and inform them about our study programmes. Every year Stockholm University welcomes thousands of international students from all over the world. Each bringing their own backpack full of experiences, expectations and dreams. Education and research are closely linked at Stockholm University. As a student, you will have direct contact with leading researchers in your field and access to the most recent scientific findings. Are you wondering what former students thought about your programme? Are you curious about what your education can lead to after graduation? Hear from our alumni!