Deep Learning (DL)
Course content
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.
BSc Programme in Machine Learning and Data Science
MSc Programme in Computer Science
MSc Actuarial Mathematics
MSc Mathematics-Economics
Msc Statistics
Knowledge of
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Convolutional neural networks
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Transformers
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Message passing and graph neural networks
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Generative neural networks such as variational autoencoders
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Basic strategies for interpretability of deep neural networks
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Training methodology
Skills to
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Select appropriate methodology to solve deep learning problems
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Implement selected deep learning algorithms
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Design and train deep learning algorithms
Competences to
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Reflect upon the capabilities and limitations of deep learning algorithms
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Recognise and describe possible applications of deep learning methodology
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Design, optimise and use deep models
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Apply the learned methodology to applications in analysis of real-world data such as images, sound and text
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Analyse deep learning algorithms
The course will mix lectures, exercise classes, and project work.
See Absalon for course litterature.
Academic qualifications corresponding to:
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.
The course is similar to the discontinued courses Advanced Deep
Learning (NDAK22002U and NDAB21009U) and Artifical Intelligence
(NDAB20002U).
Therefore you cannot register for this course, if you have already
passed Advanced Deep Learning (NDAK22002U or NDAB21009U) or
Artifical Intelligence (NDAB20002U).
- ECTS
- 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 assessment
See Learning outcome
Single subject courses (day)
- Category
- Hours
- Lectures
- 32
- Preparation
- 68
- Exercises
- 46
- Exam
- 60
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAK24002U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 2
- Schedulegroup
-
C
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Stefan Sommer (6-84807e7e768351757a3f7c863f757c)
Se skema
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende