Machine Learning

Faculteit Science and Engineering
Jaar 2017/18
Vakcode KIM.ML09
Vaknaam Machine Learning
Niveau(s) master
Voertaal Engels
Periode semester I b

Uitgebreide vaknaam Machine Learning
Leerdoelen The aim of this course is to get acquainted with a large number of different machine learning algorithms. These algorithms can be used for mapping particular inputs to desired categories as in supervised learning, but we will also treat unsupervised and reinforcement learning algorithms in this course.
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 data-driven 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 practicum 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)
(There will be lectures given by the lecturer and a computer practicum)
Toetsvorm Opdracht (AST), Schriftelijk tentamen (WE)
(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
Titel Auteur ISBN Prijs
Introduction to Machine Learning, 2004 Ethem Alpaydin
Entreevoorwaarden Knowledge about Calculus and Linear Algebra is necessary in order to do well for this course
Opgenomen in
Opleiding Jaar Periode Type
MSc Artificial Intelligence  (A - General Mandatory Course Units) - semester I b verplicht
MSc Astronomy  (Optional Courses in Data Science) - semester I b keuze
MSc Behavioural and Cognitive Neurosciences; Research master  (BCN Approved Modules) 2 semester I b elective
MSc Computing Science  ( Intelligent Systems and Visual Computing - guided choice courses ) - semester I b keuze
MSc Computing Science  ( Data Science and Systems Complexity - guided choice courses ) - semester I b keuze
MSc Computing Science  ( Software Engineering and Distributed Systems - guided choice courses) - semester I b keuze
MSc Human-Machine Communication  (C - Elective Course Units) - semester I b keuze
MSc Human-Machine Communication  (B - Mandatory Course Units Computational Cognitive Neuroscience) - semester I b verplicht CCN
MSc Mathematics  (Science, Business and Policy with Statistics and Big Data) - semester I b keuzegroep
MSc Mathematics  ( Statistics and Big Data: Guided Choice) - semester I b keuze
ReMa Taalwetenschappen / Linguistics  (ReMa Language and Communication Technologies (LCT); Erasmus Mundus) 1 semester I b keuze