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.

Education

BSc Programme in Machine Learning and Data Science
MSc Programme in Computer Science
MSc Actuarial Mathematics
MSc Mathematics-Economics
Msc Statistics

Learning outcome

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

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).

Written
Individual
Continuous feedback during the course of the semester
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   (6-75716f6f677442666b306d7730666d)
Saved on the 14-02-2024

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students