Deep Learning 7,5 Credits

Course Contents

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

Prerequisites

Passed courses at least 90 credits within the major subject Computer Engineering, Electrical Engineering (with relevant courses in Computer Engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering, and completed courses Artificial Intelligence, 7,5 credits, Mathematics for Intelligent Systems, 7,5 credits, and Machine Learning, 7.5 credits or equivalent. Proof of English proficiency is required.

Level of Education: Master
Course code/Ladok code: TDIS22
The course is conducted at: School of Engineering Last modified 2022-11-25 07:22:20