Deep Learning 7,5 Credits
Course ContentsThis 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 both implement various deep learning algorithms from scratch, and use modern deep learning software. 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:
- Loss/Activation Functions, Optimization, SGD, Backpropagation, Computational Graphs
- Neural Networks: Feed-Forward, Convolutional, Recurrent, GANs, Autoencoders
- Methodology for training Neural Networks
- Deep Reinforcement Learning
- Analysis, Interpretation and Evaluation of Deep Learning Models
- Ethics and Fairness in Deep Learning
- Deep Learning Applications, such as; Computer Vision, Natural Language Processing, Image Segmentation and Image Captioning
PrerequisitesPassed 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 EngineeringLast modified 2021-06-07 09:23:59