Advanced Deep Learning (ADL)
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.
BSc Programme in Machine Learning and Data Science
MSc Programme in Computer Science
Convolutional neural networks
Recurrent neural networks
Generative neural networks, such as
Generative adversarial networks (GANs)
Theory of deep learning
Topics in deep learning, exemplified by
Restricted Boltzmann machines
Fully convolutional neural networks
Graph neural networks
Deep reinforcement learning
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
Reflect upon the capabilities and limitations of deep learning algorithms
Recognising and describing 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
The course will mix lectures, exercise classes, and project work.
See Absalon for course litterature.
Academic qualifications corresponding to the previous courses on
the BSc in Machine learning & data science. As a minimum this
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 Introduktion til Machine Learning (IntroML) or Machine Learning 1 (ML1) or Machine Learning (ML). Please note that these courses include basic deep learning.
5. Programming experience in Python.
The course is equivalent to NDAB20002U Artificial Intelligence
(AI). It is not allowed to pass AI and take Advanced Deep Learning
The course is identical to the discontinued course NDAB20002U Advanced Deep Learning (ADL). Therefore you cannot register for NDAB21009U - Advanced Deep Learning (ADL), if you have already passed NDAB20002U Advanced Deep Learning (ADL).
If you are registered with examination attempts in NDAB20002U Advanced Deep Learning (ADL) without having passed the course, you have to use your last examination attempts to pass the exam in NDAB21009U - Advanced Deep Learning (ADL). You have a total of three examination attempts.
- 7,5 ECTS
- Type of assessment
Continuous assessmentContinuous assessment of 4 written assignments. All assignments must be passed. The final grade is based on an overall assessment.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
According to learning outcomes.
Single subject courses (day)
- Course number
- 7,5 ECTS
- Programme level
- Block 4
- No limit.
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Stefan Horst Sommer (6-787472726a7745696e33707a336970)
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Courseinformation of students