Applied deep learning, 5 credits

Tillämpad djupinlärning


Language of instruction: English
Course period: Period 2
Course structure: The course has both online and campus/in-person elements. A large part of the course is on-demand content and practical exercises. The content and exercises will be discussed in weekly in-person meetings. The meetings will be offered both via zoom and in-person so that fully remote participation is possible to better accommodate Ph.D. students from different sections.


Basic mathematical background in linear algebra as well as basic knowledge of the Python programming language is a benefit but there is also room to improve your programming skills. You will need to use Python for all homework assignments. Everyone without Python experience is highly encouraged to work through an online course on Python with an emphasis of the numpy package. Many good online resources are available, google yourself or choose one of the following:


After successful completion of this course, students will be able to:
1. summarize the concepts of deep learning
2. apply deep learning for typical problems in their field of research
3. design and optimize network architectures for different problems


A large part of the work of many Ph.D. students is data analysis. This course will empower the students to plan and use appropriate methods by equipping the students with state-of-the-art techniques in machine learning. Machine learning made tremendous progress over the last few years. The advances in computing resources, together with the development of algorithms to efficiently train deep neural networks (deep learning), results in an unpreceded performance, most notably in the advances in image recognition. Deep learning can be applied to a vast variety of problems in different areas and will often outperform existing techniques.
The students will learn this powerful research method. The course will focus on the practical skills of applying deep learning to their domain-specific problems. The discussion of many research examples will help the students to undertake qualified tasks in predetermined time frames. Through the discussion of weekly exercises, the students will learn to review and evaluate their work.


Fundamentals of Deep Learning; Generalization, Regularization and Validation; Optimization and Hyperparameter Tuning; Convolutional Neutral Networks, Recurrent Neural Networks; Classification and Regression Tasks; Visualization & Advanced Computer Vision Methods; Autoencoders; Generative models, variational autoencoders, generative adversarial networks; Research examples from different fields.


This course will be given in a novel way. Instead of weekly lectures, the content will be split up into smaller 15-20min chunks and delivered via on-demand videos. Each of these teaching units will be accompanied with a self-correcting quiz and a practical exercise (hand-in assignments) where the students apply the learning content to a typical problem. This direct application will enhance the learning outcome and will equip the students with the skills to apply deep learning to their own problems. Questions on the content or exercises, as well as their correct solutions, will be discussed in a weekly 1.5h meeting (either in-person or zoom).


Via hand-in assignments. A successful participation in the exercises is required to pass the course. Weekly exercises where the students will apply the lecture content to practical problems will be distributed. The exercises will typically involve implementing a network architecture, training of this architecture on data and iteratively improving the network.


Christian Glaser,


Physics and Astronomy


Christian Glaser,


Submit the application for admission to:
Submit the application not later than: End of September

Last modified: 2023-11-10