Neural Information Processing Systems
Course infoSchedule
Course moduleSOW-MKI49
Credits (ECTS)6
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
prof. dr. M.A.J. van Gerven
Other course modules lecturer
Contactperson for the course
prof. dr. M.A.J. van Gerven
Other course modules lecturer
prof. dr. M.A.J. van Gerven
Other course modules lecturer
Academic year2020
SEM1-SEM2  (01/09/2020 to 16/07/2021)
Starting block
Course mode
Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course.
RemarksOpen to AI, CNS (Brain Networks and Neuronal Communication) and CS (Data Science) Master students
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
While AI has made rapid progress, there still remains a large gap between machine intelligence and natural intelligence. In this course, you will learn about recent developments in the fields of machine learning and neuroscience that provide insights into the principles and mechanisms that govern natural intelligence. Ultimately, this leads to a better understanding of neural information processing in biological systems and paves the way for the development of more capable machine intelligence. After completion of the course, the student is able to:
  • Understand the principles that shape neural information processing systems
  • Describe relevant models and theories in machine learning and neuroscience
  • Implement computational models and algorithms for learning, inference and control in neural systems
  • Train artificial agents to maximise reward in complex environments
We consider natural agents as adaptive systems that strive to maximise reward in complex stochastic environments. We formalise the interaction between agents and their environments in terms of partially-observable Markov decision processes. Next, we investigate how neural information processing systems are shaped by neuron models, objective functions, learning algorithms and network architectures. Finally, we consider how natural agents can learn to solve tasks using recent developments in e.g. reinforcement learning and self-supervised learning. You will learn to implement several models and algorithms and we will discuss recent papers in the field.
Presumed foreknowledge

This course requires experience with the Python programming language, prior experience with neural networks and basic mathematical skills (calculus, linear algebra, probability theory).

Test information
  • Practical assignment (50%)
  • Written exam (50%)
There is only a resit for the written exam.

Recommended materials
Literature list
Selected papers

Instructional modes
Attendance MandatoryYes

Attendance MandatoryYes

Practical sessions
Attendance MandatoryYes

Final grade
Test weight1
Test typeExam
OpportunitiesBlock SEM1, Block SEM2