Programme Outlines and Overviews

This is an introductory course in Deep Learning. The course covers basic and state-of-the-art algorithms for training various deep neural network architectures, alternating theory with practice. The course includes assignments where the students implement various deep learning algorithms. After completing the course, the student shall have acquired a thorough theoretical understanding of, and practical experience with, modern algorithms for deep learning, applied on common deep learning tasks. Specifically, the student should understand and be able to apply all theoretical concepts covered. The course includes the following elements: - Methodology for training Neural Networks - Neural Network Architectures: Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Graph Convolutional Neural Networks - Learning from no or little data: Unsupervised, Weakly-supervised, Self-supervised learning, as well as Few and Zero-shot learning - Deep Generative Models: Generative Adversarial Networks and Variational Autoencoders - Deep Reinforcement Learning - Explainable Deep Learning Models - Computer Vision Applications: Object Detection, Semantic Segmentation, Image Captioning, Visual Question Answering - Evaluation of Deep Learning Models