Deep Learning
Course infoSchedule
Course moduleNWI-IMC070
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Informatica en Informatiekunde;
PreviousNext 2
dr. I.G. Bucur
Other course modules lecturer
A. Kolmus
Other course modules lecturer
dr. T.M. van Laarhoven
Other course modules lecturer
Contactperson for the course
dr. T.M. van Laarhoven
Other course modules lecturer
dr. T.M. van Laarhoven
Other course modules lecturer
Academic year2023
KW1-KW2  (04/09/2023 to 28/01/2024)
Starting block
Course mode
RemarksThis is the new 6-ec course that succeeds the 3-ec course NWI-IMC05 Deep Learning.
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
After completing this course, you will be able to:
  • implement basic deep learning models from scratch.
  • implement deep neural networks and training algorithms using PyTorch.
  • implement the stochastic gradient descend algorithm, and describe and use its variants.
  • understand, train, and use convolutional neural networks.
  • understand, train, and use recurrent neural networks.
  • understand, train, and use transformer networks.
  • understand, train, and use self-supervised neural network methods.
  • understand, train, and use generative neural network models.
  • decide which neural network methods are appropriate for a given use case.
  • evaluate neural networks, to diagnose problems and to compare different methods.
  • read and understand academic literature about deep learning.

Deep Learning is a flavor of machine learning that uses deep artificial
neural networks, meaning networks with many layers and often millions of
parameters. Over the last couple of years Deep Learning has shown huge
successes in different applications such as image and speech
recognition, game playing agents, image synthesis, computational
biology, etc.
In this course you will learn both how to apply Deep Learning to solve
problems, as well as how these deep neural networks are implemented and
how they work. We will treat many different architectures, and show
which ones are appropriate in which situations. Because this is a very
broad field that is continuously developing, we will not be focusing on
any specific application, but rather lay the groundwork on which all
deep learning techniques are built.

Presumed foreknowledge
This is a masters level machine learning course, and prior machine learning knowledge is expected. It is sufficient to have previously taken the course Data Mining (NWI-IBI008), or an equivalent course in another bachelor's program.

In addition, students are expected to have a working knowledge of
  • Linear algebra (vectors, matrices, matrix multiplication)
  • Calculus (derivatives, chain rule)
  • Probability theory (distributions, Bayes rule)
Some programming experience is required. Familiarity with Python is helpful, but is not expected.
Test information
The final grade is determined by
  • Written exam (50%)
  • Practical assignments (50%)
The grades for the written exam and the practical assignment must both be at least a 5. The final grade must be at least 5.5 (which will be rounded up to 6).
This course succeeds the 3-ec course NWI-IMC058 Deep Learning.
Instructional modes
Attendance MandatoryYes

Written exam
Test weight1
Test typeExam
OpportunitiesBlock KW2, Block KW3

Practical assignments
Test weight1
Test typeAssignment
OpportunitiesBlock KW2