Faculteit  Science and Engineering 
Jaar  2018/19 
Vakcode  KIM.ML09 
Vaknaam  Machine Learning 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Machine Learning  
Leerdoelen  At the end of the course, the student is able to:  Understand the main principles of machine learning and the differences between different algorithms  Understand the ideas of supervised learning and to be able to understand which operations a machine learning algorithm performs for supervised learning problems.  Understand the ideas of unsupervised learning and to be able to understand which operations a machine learning algorithm performs for unsupervised learning problems.  Understand the ideas of reinforcement learning and to be able to understand which operations a reinforcement learning algorithm performs  Develop, implement and perform experiments with a machine learning algorithm. 

Omschrijving  Learning is an essential part of intelligence. It makes it possible to cope with uncertain environments or domains about which one has insufficient knowledge to completely model it. Machine learning algorithms are usually datadriven which means that the algorithms learn functions mapping inputs to desired outputs based on example data. In this course we will treat a wide variety of machine learning algorithms such as decision trees, neural networks, support vector machines, and reinforcement learning algorithms. There will be a practical where students will implement their own machine learning system and they will write a report about this system and the obtained results. Furthermore, there will be an examination at the end of the course. 

Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Practisch werk (PRC), Werkcollege (T)
(There will be lectures given by the lecturer and a computer practicum.) 

Toetsvorm 
Schriftelijk tentamen (WE), Verslag (R)
(The examination will count for 50% of the final mark and the practical report also for 50%. The grade of the exam needs to be higher or equal to 5.0 in order to pass this course.) 

Vaksoort  master  
Coördinator  dr. M.A. Wiering  
Docent(en)  dr. M.A. Wiering  
Verplichte literatuur 


Entreevoorwaarden  Knowledge about Calculus and Linear Algebra is necessary in order to do well for this course.  
Opmerkingen  Taking this course helps with preparing for Deep Learning (WMAI18002).  
Opgenomen in 
