SOW-BKI230A
Neural Networks
Course infoSchedule
Course moduleSOW-BKI230A
Credits (ECTS)6
Category-
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
Lecturer(s)
Coordinator
prof. dr. M.A.J. van Gerven
Other course modules lecturer
Examiner
prof. dr. M.A.J. van Gerven
Other course modules lecturer
Contactperson for the course
L.E.C. Jacques
Other course modules lecturer
Academic year2017
Period
PER3-PER4  (05/02/2018 to 13/07/2018)
Starting block
PER3
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
Upon completion of the course, students will:
  • Have gained an overview of artificial neural networks.
  • Be able to place the domain of neural networks in a historical context and conclude different principles underlying classic and modern artificial neural networks.
  • Be able to describe behaviour of different models in terms of formal properties.
  • Be able to implement various neural network models in Python.
Content
During the lectures, the formal concepts underlying modern neural networks will be developed, including deep neural networks and recurrent neural networks. Also, various classic neural network models will be discussed like the (multi-layer) Perceptron, Hopfield networks and Boltzmann machines. During the practicals, students will get to immers themselves in the theoretical and practical aspects of neural networks. Students will get to implement various models using Python, a programming language which they will learn to use during the course.
Levels
AI-B2

Test information
Practical assignments and written exam
The final grade will be a weighted average of the assignments and the exam.

Prerequisites
Students will need to have basic knowledge of calculus, probability theory, lineair algebra and possess basic programming skills.

Contact information
Dr. M. van Gerven, T: 024-3655931, E: m.vangerven@donders.ru.nl

Recommended materials
Course material
Lecture notes

Instructional modes
Lecture

Supervised computer practicals

Tests
Exam
Test weight1
OpportunitiesBlock HERT, Block PER4