NDAK22002U Advanced Deep Learning (ADL)
BSc Programme in Cognitive Data Science
BSc Programme in Machine Learning and Data Science
MSc Programme in Computer Science
MSc Actuarial Mathematics
MSc Mathematics-Economics
Msc Statistics
Deep learning has pushed the boundaries in Artificial Intelligence (AI) and has been outperforming the state-of-the-art in numerous applications across a wide range of domains. These include object classification in images, information retrieval along with web search, natural language processing tasks such as automatic translation, and bioinformatics. This course will give you detailed insight into deep learning, covering algorithms, theory and tools in this exciting field.
Knowledge of
Convolutional neural networks
Recurrent neural networks
Generative neural networks, such as
Variational autoencoders
Generative adversarial networks (GANs)
Theory of deep learning
Topics in deep learning, exemplified by
Fully convolutional neural networks
Graph neural networks
Representation learning
Diffusion models
Self-supervised learning
Skills to
Select appropriate methodology to solve deep learning problems
Implement selected deep learning algorithms using state-of-the-art tools
Design and train deep learning algorithms
Competences to
Reflect upon the capabilities and limitations of deep learning algorithms
Recognise and describe possible applications of deep learning methodology
Design, optimise and use advanced deep models
Apply the learned methodology to applications in analysis of real-world data such as images, sounds and text
Analyse deep learning algorithms
See Absalon for course litterature.
1. Linear algebra corresponding to the course Lineær Algebra i datalogi (LinAlgDat).
2. Calculus corresponding to the courses Introduktion til matematik i naturvidenskab (MatIntroNat) and Matematisk Analyse (MatAn).
3. Basic statistics and probability theory corresponding to the course Sandsynlighedsregning og statistik (SS).
4. Machine learning corresponding to Machine Learning A (MLA). Please note that these courses include basic deep learning.
5. Programming experience in Python.
- Category
- Hours
- Lectures
- 32
- Preparation
- 68
- Exercises
- 46
- Exam
- 60
- Total
- 206
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessment
- Type of assessment details
- Continuous assessment of 3-4 written assignments. All assignments must be passed. The final grade is based on an overall assessment.
- Aid
- All aids allowed
For programming tasks specifically, the use of GitHub Copilot or similar AI-based programming tools is permitted.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
The re-exam is 25 minutes oral examination, without preparation, in full course syllabus.
Criteria for exam assesment
According to learning outcomes.
Course information
- Language
- English
- Course code
- NDAK22002U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- C
- Course capacity
- No limit.
The number of seats may be reduced in the late registration period
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinators
- Stefan Sommer (6-7b7775756d7a486c7136737d366c73)