NDAK24003U Advanced Topics in Deep Learning (ATDL)
MSc Programme in Computer Science
This course will give you detailed insight into advanced deep learning methods and techniques, covering algorithms, theory and tools in this exciting and fast advancing field.
This is an advanced topics course, and the exact list of topics will therefore change from year to year, depending on current trends in the literature.
The course is on advanced topics, and it therefore has the introductory machine learning and deep learning courses as prerequisites.
Knowledge of
Selected advanced topics in deep learning, including:
state-or-the-art deep models in selected domains
design of deep learning algorithms
analysis of deep learning algorithms
theory of deep learning
The exact list of topics will depend on the teachers and trends
in deep learning research. They will be announced on the
course's Absalon page.
Skills to
Implement selected advanced deep learning algorithms using state-of-the-art tools
Read and understand recent scientific literature in the field of deep learning
Apply the knowledge obtained by reading scientific papers
Compare deep learning methods and assess their potentials and shortcomings
Competences to
Understand advanced deep learning methods and techniques
Design, optimize and use advanced deep models
Plan and carry out self-learning
See Absalon.
- Machine learning corresponding to the courses Machine Learning A (MLA) and Deep learning (DL).
- Solid programming experience in Python.
- Linear algebra corresponding to the course Linear Algebra in Computer Science (LinAlgDat).
- Calculus corresponding to the courses Mathematical Analysis and Probability Theory for Computer Scientists (MASD) and Modelling and Analysis of Data (MAD), or Introduction to Mathematics for Science (MatIntroNat) and Mathematical Analysis (MatAn), or equivalent.
- Statistics and probability theory corresponding to the course Probability Theory and Statistics (SS).
- Category
- Hours
- Lectures
- 28
- Class Instruction
- 14
- Preparation
- 70
- Exercises
- 94
- 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
Internal examiner.
- 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
- NDAK24003U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- A
- 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 (6-787472726a7745696e33707a336970)