In this course, you will be introduced to the foundations of deep learning, the most effective and common network structures and how to build, train and evaluate deep neural networks for different applications.
Course description for study year 2022-2023. Please note that changes may occur.
In this course, you will be introduced to the foundations of deep learning, the most effective and common network structures and their applications and how to build, train and evaluate deep neural networks for different applications. This includes:
Neurons, layers, back propagation, optimizers, loss functions, hyperparameters
Unsupervised, supervised and semi-supervised learning approaches.
Multilayer Perceptron Network (MPN)
Convolutional Neural Network (CNN)
Time Series analysis
Reccurent Neural Network (RNN) and Long Short-Term Memory networks (LSTMs)
Natural language processing (NLP)Natural Language Understanding (NLU) and Embeddings.
Image classification and Object detection
Video Activity recognition
Deep Belief Networks
Deep Reinforcement Learning
Deep learning in image reconstruction and medical imaging.
At the end of this course the student should have a fundamental understanding of methods and deep neural network structures commonly used in deep learning. The student should also be able to build, train and evaluate models for one or more practical deep learning problems.
Required prerequisite knowledge
ELE520 Machine Learning
Form of assessment
The assigned project is carried out in groups of two students. Exceptionally, it can be one or three students per group. The report describes and documents work in the project. The report is made in collaboration with all the participants in the group and all participants will get the same grade. An oral presentation of the project is a mandatory part of the project.There is no resit exam in this course. A new project report must be submitted the next time the course is taught.
2 of 2 assignments need to be approved by course instructor within the specified deadlines.
The course has a duration of approximately 8 weeks and will be completed mid October. Lectures will be held the first 5 weeks. The students are expected to spend additional 6-8 hours a week on self-study and assignments.
The project will be carried out in the last three weeks of the course and it is expected that each student spend about 15 hours per week.