Neural Networks and Computational Intelligence
Faculteit  Science and Engineering 
Jaar  2019/20 
Vakcode  WMCS15001 
Vaknaam  Neural Networks and Computational Intelligence 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Neural Networks and Computational Intelligence  
Leerdoelen  At the end of the course, the student is able to: 1) implement and use various types of artificial neural network 2) relate artificial neural networks to their biological background and different levels of modelling biological systems 3) explain, express mathematically, implement and apply basic training schemes 4) explain the concept of supervised learning from examples and are able to apply it in terms of simple example situations 5) to explain and to implement and make use of standard validation and regularization methods in practical situations 6) perform computer simulations of machine learning processes and present and discuss results thereof according to scientific standards. 7) work in teams (pairs) on scientific projects and reports 

Omschrijving  This module provides an introduction to neural networks and related concepts in machine learning. We will discuss different types of network architectures and their usefulness and limitations in classification or regression problems. In this context, the corresponding training algorithms will be the focus of our attention. Besides their practical implementation, we will address theoretical aspects, e.g. with respect to their convergence behaviour. The list of topics includes: perceptron training, support vector machines, gradientbased training, testing and validation methods, multilayered neural networks, shallow and deep networks, alternative architectures.  
Uren per week  
Onderwijsvorm  Hoorcollege (LC), Practisch werk (PRC)  
Toetsvorm 
Schriftelijk tentamen (WE), Verslag (R)
(Final grade (FG): weighted average (WA) of the grade for the exam (70%) and an averaged grade for three assignments (30%). WAs above 4.75 and below 5.75 will result in a FG of 5.0, in all other cases the FG is rounded to the nearest halfinteger. However, in order to pass the course, both partial grades have to be at least 5.5. If this is not the case, the FG is 5.0 or lower according to the WA.) 

Vaksoort  master  
Coördinator  prof. dr. M. Biehl  
Docent(en)  prof. dr. M. Biehl  
Verplichte literatuur 


Entreevoorwaarden  The course unit assumes prior knowledge in the sense that basic programming skills in one of the major programming languages and/or tools like Matlab or Mathematica have been acquired. Having attended courses like Introduction to Intelligent Systems or Introduction to Artificial Intelligence is beneficial but not required.  
Opmerkingen  This course has LIMITED CAPACITY for students outside of the CS programme:


Opgenomen in 
