Introduction to Intelligent Systems

Faculteit Science and Engineering
Jaar 2019/20
Vakcode INBINTS-08
Vaknaam Introduction to Intelligent Systems
Niveau(s) bachelor
Voertaal Engels
Periode semester I a

Uitgebreide vaknaam Introduction to Intelligent Systems
Leerdoelen At the end of the course, the student is able to:
1. Obtain an overview of the main methods and techniques used in pattern recognition and machine learning
2. Basic problems of machine learning and pattern recognition
3. Select appropriate techniques for particular tasks
4. Implement basic learning and validation methods and apply them to practical data sets.
5. Obtain insight into some basic operations in image processing
Omschrijving Computer Vision: A very brief introduction to computer vision is given. Then edge detection and morphological image processing are treated in more detail. At the end of the course students should be familiar with and able to apply basic methods of, for instance, segmentation, morphological image processing, and edge detection.

Pattern Recognition: General ideas and basic techniques of pattern recognition are considered, such as statistical decision theory and hypothesis testing, Bayesian classification, parametric classification and normal distributions, non-parametric classification and the knn-method. Algorithms for hierarchical clustering are presented. Finally, decision trees are also presented. Theories and methods are illustrated by practical applications.

Machine Learning: Basic ideas of learning systems are presented and discussed referring to biological learning and neural networks. Machine learning is outlined and explained mainly in terms of classification problems, with emphasis on distance-based methods such as Learning Vector Quantization as an example framework. Validation methods and the problem of over-fitting are briefly discussed in terms of classification and regression problems. Methods are also illustrated by practical applications.
Uren per week 4
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
Toetsvorm Practisch werk (PR), Schriftelijk tentamen (WE)
(Final grade: weighted average of the marks for the practicals (40%) and the written (digital) exam (60%). In order to pass, students must get a mark of at least 5.5 for both of these separately, and the weighted average has to be at least 5.75. Weighted averages above 4.75 and below 5.75 will result in a final grade 5.0, in all other cases the grade is rounded to the nearest half-integer value.)
Vaksoort bachelor
Coördinator prof. dr. M. Biehl
Docent(en) prof. dr. M. Biehl , E. Talavera Martínez, MSc.
Verplichte literatuur
Titel Auteur ISBN Prijs
All material is provided in the form of handouts and addtional information (links, articles etc.) in Nestor
Entreevoorwaarden Basic programming skills, knowledge of mathematics and statistics that is typical of the respective courses included in standard bachelor CS curricula.
Opmerkingen The computer practicals are in Matlab. It would be helpful if students have some programming experience in Matlab. Otherwise, they should quickly acquire some Matlab programming skills by following an on-line tutorial (many are available on internet). This is not difficult because Matlab is easy to learn on the background of some programming skills in another programming language.
Opgenomen in
Opleiding Jaar Periode Type
BSc Artificial Intelligence 3 semester I a keuze
BSc Computing Science  (Specializing Minor Computing Science) 3 semester I a keuze
BSc Courses for Exchange Students: Artificial Intelligence & Computing Science - semester I a Computing Science