NDAK24002U Deep Learning (DL)
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 and natural language processing tasks such as automatic translation. This course will give you insight into the foundational methods in deep learning and techniques for effectively training deep networks.
Knowledge of
Convolutional neural networks
Transformers
Message passing and graph neural networks
Generative neural networks such as variational autoencoders
Basic strategies for interpretability of deep neural networks
Training methodology
Skills to
Select appropriate methodology to solve deep learning problems
Implement selected deep learning algorithms
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 deep models
Apply the learned methodology to applications in analysis of real-world data such as images, sound 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 this course includes basic deep learning.
5. Programming experience in Python.
Therefore you cannot register for this course, if you have already passed Advanced Deep Learning (NDAK22002U or NDAB21009U) or Artifical Intelligence (NDAB20002U).
- 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, this includes AI-based programming tools such as github copilot or similar.
- 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
See Learning outcome
Course information
- Language
- English
- Course code
- NDAK24002U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- C
- Course capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinators
- Stefan Sommer (6-797573736b78466a6f34717b346a71)