Deep Learning

Aim

The overall aim of the course is to give the student a good theoretical understanding and good practical skills in machine learning based on deep neural networks. On completion of the course, the student should be able to:

1. Use central theoretical concepts to describe and account for differences between models and methods in deep learning

2. Use relevant models and methods from deep learning to formulate, structure and solve practical problems involving large and complex data sets

3. Choose appropriate evaluation protocols and critically evaluate the quality of the solutions

4. Analyze and criticize the choice of models and methods

5. Describe ethical issues and societal aspects related to deep learning.

Content

The course introduces key concepts in deep learning, from basic to state-of-the-art.

This includes: Common model types (e.g., MLP, CNN, RNN, Transformers); Optimization and learning algorithms for deep networks; Representational learning and self-supervised learning; Generative models; Different application areas of deep learning, in e.g. natural language processing and text analysis, computer vision, and natural sciences; Ethical considerations and societal aspects linked to deep learning.